EDLD 652 Final Project Draft

mathlea_10 <- get_data("EDFacts_math_achievement_lea_2010_2019")

rlalea_10 <- get_data("EDFacts_rla_achievement_lea_2010_2019")

fiscal2010 <- get_data("NCES_CCD_fiscal_district_2010")

Data Visualization 1

Rebecca

Research Question:

How do high school students’ subgroup makeup (i.e., Race/ethnicity, Male vs. Female, economically disadvantaged, Limited English, Migrant status, Disability status, and Homelessness) differ among states/regions?

Datasets:

• EDFacts_math_achievement_lea_2010_2019
• EDFacts_rla_achievement_lea_2010_2019
• EDFacts_rla_participation_lea_2013_2019
• EDFacts_math_participation_lea_2013_2019

skim(mathlea_10)

Data summary

Name	mathlea_10
Number of rows	15747
Number of columns	232
_______________________
Column type frequency:
character	118
numeric	113
POSIXct	1
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
ALL_MTH00PCTPROF	0	1.00	1	5	0	134	0
ALL_MTH03PCTPROF	1367	0.91	2	5	0	114	0
ALL_MTH04PCTPROF	1368	0.91	2	5	0	122	0
ALL_MTH05PCTPROF	1356	0.91	2	5	0	122	0
ALL_MTH06PCTPROF	1354	0.91	1	5	0	123	0
ALL_MTH07PCTPROF	1779	0.89	1	5	0	125	0
ALL_MTH08PCTPROF	1836	0.88	1	5	0	129	0
ALL_MTHHSPCTPROF	3666	0.77	1	5	0	131	0
CWD_MTH00PCTPROF	189	0.99	1	5	0	127	0
CWD_MTH03PCTPROF	2014	0.87	2	5	0	105	0
CWD_MTH04PCTPROF	2002	0.87	1	5	0	111	0
CWD_MTH05PCTPROF	2006	0.87	1	5	0	116	0
CWD_MTH06PCTPROF	2031	0.87	1	5	0	108	0
CWD_MTH07PCTPROF	2384	0.85	1	5	0	108	0
CWD_MTH08PCTPROF	2463	0.84	1	5	0	114	0
CWD_MTHHSPCTPROF	4155	0.74	1	5	0	108	0
ECD_MTH00PCTPROF	405	0.97	1	5	0	129	0
ECD_MTH03PCTPROF	1792	0.89	2	5	0	116	0
ECD_MTH04PCTPROF	1776	0.89	2	5	0	120	0
ECD_MTH05PCTPROF	1754	0.89	1	5	0	123	0
ECD_MTH06PCTPROF	1772	0.89	1	5	0	123	0
ECD_MTH07PCTPROF	2169	0.86	1	5	0	124	0
ECD_MTH08PCTPROF	2215	0.86	1	5	0	128	0
ECD_MTHHSPCTPROF	3952	0.75	1	5	0	130	0
FIPST	0	1.00	2	2	0	51	0
F_MTH00PCTPROF	47	1.00	1	5	0	125	0
F_MTH03PCTPROF	1459	0.91	2	5	0	105	0
F_MTH04PCTPROF	1467	0.91	2	5	0	114	0
F_MTH05PCTPROF	1463	0.91	2	5	0	115	0
F_MTH06PCTPROF	1460	0.91	1	5	0	117	0
F_MTH07PCTPROF	1877	0.88	1	5	0	119	0
F_MTH08PCTPROF	1933	0.88	1	5	0	127	0
F_MTHHSPCTPROF	3743	0.76	1	5	0	125	0
FILEURL	0	1.00	88	88	0	1	0
HOM_MTH00PCTPROF	9382	0.40	2	5	0	95	0
HOM_MTH03PCTPROF	10803	0.31	2	5	0	40	0
HOM_MTH04PCTPROF	10855	0.31	2	5	0	39	0
HOM_MTH05PCTPROF	10855	0.31	2	5	0	41	0
HOM_MTH06PCTPROF	10923	0.31	1	5	0	40	0
HOM_MTH07PCTPROF	11042	0.30	1	5	0	39	0
HOM_MTH08PCTPROF	11069	0.30	1	5	0	40	0
HOM_MTHHSPCTPROF	11704	0.26	1	5	0	46	0
LEAID	0	1.00	7	7	0	15747	0
LEANM09	0	1.00	3	60	0	15465	0
LEP_MTH00PCTPROF	4294	0.73	1	5	0	125	0
LEP_MTH03PCTPROF	6354	0.60	2	5	0	110	0
LEP_MTH04PCTPROF	6505	0.59	1	5	0	112	0
LEP_MTH05PCTPROF	6738	0.57	1	5	0	116	0
LEP_MTH06PCTPROF	6940	0.56	1	5	0	103	0
LEP_MTH07PCTPROF	7267	0.54	1	5	0	104	0
LEP_MTH08PCTPROF	7406	0.53	1	5	0	102	0
LEP_MTHHSPCTPROF	8633	0.45	1	5	0	85	0
MAM_MTH00PCTPROF	4167	0.74	2	5	0	99	0
MAM_MTH03PCTPROF	8056	0.49	2	5	0	43	0
MAM_MTH04PCTPROF	8050	0.49	2	5	0	45	0
MAM_MTH05PCTPROF	8049	0.49	2	5	0	43	0
MAM_MTH06PCTPROF	7977	0.49	2	5	0	41	0
MAM_MTH07PCTPROF	8141	0.48	2	5	0	41	0
MAM_MTH08PCTPROF	8129	0.48	2	5	0	40	0
MAM_MTHHSPCTPROF	8926	0.43	2	5	0	43	0
MAS_MTH00PCTPROF	3309	0.79	2	5	0	92	0
MAS_MTH03PCTPROF	6732	0.57	2	5	0	61	0
MAS_MTH04PCTPROF	6711	0.57	2	5	0	63	0
MAS_MTH05PCTPROF	6747	0.57	2	5	0	68	0
MAS_MTH06PCTPROF	6816	0.57	2	5	0	70	0
MAS_MTH07PCTPROF	7023	0.55	2	5	0	75	0
MAS_MTH08PCTPROF	7065	0.55	2	5	0	80	0
MAS_MTHHSPCTPROF	7983	0.49	2	5	0	81	0
MBL_MTH00PCTPROF	1951	0.88	1	5	0	126	0
MBL_MTH03PCTPROF	5389	0.66	2	5	0	109	0
MBL_MTH04PCTPROF	5307	0.66	2	5	0	108	0
MBL_MTH05PCTPROF	5249	0.67	2	5	0	118	0
MBL_MTH06PCTPROF	5239	0.67	2	5	0	114	0
MBL_MTH07PCTPROF	5579	0.65	1	5	0	115	0
MBL_MTH08PCTPROF	5618	0.64	1	5	0	122	0
MBL_MTHHSPCTPROF	6772	0.57	1	5	0	125	0
MHI_MTH00PCTPROF	1348	0.91	2	5	0	121	0
MHI_MTH03PCTPROF	4108	0.74	2	5	0	103	0
MHI_MTH04PCTPROF	4194	0.73	2	5	0	110	0
MHI_MTH05PCTPROF	4179	0.73	2	5	0	111	0
MHI_MTH06PCTPROF	4231	0.73	1	5	0	116	0
MHI_MTH07PCTPROF	4505	0.71	1	5	0	115	0
MHI_MTH08PCTPROF	4630	0.71	1	5	0	127	0
MHI_MTHHSPCTPROF	6008	0.62	1	5	0	118	0
MIG_MTH00PCTPROF	10326	0.34	2	5	0	85	0
MIG_MTH03PCTPROF	11597	0.26	2	5	0	35	0
MIG_MTH04PCTPROF	11613	0.26	2	5	0	36	0
MIG_MTH05PCTPROF	11628	0.26	2	5	0	36	0
MIG_MTH06PCTPROF	11676	0.26	2	5	0	36	0
MIG_MTH07PCTPROF	11814	0.25	2	5	0	30	0
MIG_MTH08PCTPROF	11817	0.25	2	5	0	37	0
MIG_MTHHSPCTPROF	12362	0.21	2	5	0	38	0
MTR_MTH00PCTPROF	9021	0.43	2	5	0	92	0
MTR_MTH03PCTPROF	10440	0.34	2	5	0	40	0
MTR_MTH04PCTPROF	10527	0.33	2	5	0	42	0
MTR_MTH05PCTPROF	10507	0.33	2	5	0	41	0
MTR_MTH06PCTPROF	10600	0.33	2	5	0	39	0
MTR_MTH07PCTPROF	10776	0.32	2	5	0	44	0
MTR_MTH08PCTPROF	10834	0.31	2	5	0	45	0
MTR_MTHHSPCTPROF	11647	0.26	2	5	0	47	0
MWH_MTH00PCTPROF	430	0.97	2	5	0	122	0
MWH_MTH03PCTPROF	1889	0.88	2	5	0	101	0
MWH_MTH04PCTPROF	1889	0.88	2	5	0	111	0
MWH_MTH05PCTPROF	1912	0.88	2	5	0	115	0
MWH_MTH06PCTPROF	1877	0.88	2	5	0	113	0
MWH_MTH07PCTPROF	2251	0.86	2	5	0	116	0
MWH_MTH08PCTPROF	2281	0.86	2	5	0	120	0
MWH_MTHHSPCTPROF	3955	0.75	2	5	0	119	0
M_MTH00PCTPROF	28	1.00	1	5	0	124	0
M_MTH03PCTPROF	1428	0.91	2	5	0	106	0
M_MTH04PCTPROF	1418	0.91	2	5	0	115	0
M_MTH05PCTPROF	1412	0.91	2	5	0	115	0
M_MTH06PCTPROF	1410	0.91	1	5	0	117	0
M_MTH07PCTPROF	1830	0.88	1	5	0	125	0
M_MTH08PCTPROF	1891	0.88	1	5	0	126	0
M_MTHHSPCTPROF	3712	0.76	1	5	0	122	0
STNAM	0	1.00	4	24	0	51	0
PIPELINE	0	1.00	38	38	0	1	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
ALL_MTH00NUMVALID	0	1.00	1627.91	6374.70	0	149	447.0	1284.00	355121	▇▁▁▁▁
ALL_MTH03NUMVALID	1367	0.91	255.01	961.48	0	28	74.0	201.00	51775	▇▁▁▁▁
ALL_MTH04NUMVALID	1368	0.91	254.95	951.13	0	28	75.0	202.00	51736	▇▁▁▁▁
ALL_MTH05NUMVALID	1356	0.91	251.32	935.44	0	28	75.0	202.00	51537	▇▁▁▁▁
ALL_MTH06NUMVALID	1354	0.91	249.75	918.15	0	28	76.0	204.00	49264	▇▁▁▁▁
ALL_MTH07NUMVALID	1779	0.89	254.91	934.73	0	30	80.0	211.00	49880	▇▁▁▁▁
ALL_MTH08NUMVALID	1836	0.88	258.21	929.41	0	30	81.0	214.00	50322	▇▁▁▁▁
ALL_MTHHSNUMVALID	3666	0.77	325.95	1284.57	0	39	99.0	248.00	50607	▇▁▁▁▁
CWD_MTH00NUMVALID	189	0.99	214.49	887.63	0	21	60.0	175.00	58764	▇▁▁▁▁
CWD_MTH03NUMVALID	2014	0.87	35.38	138.93	0	4	11.0	29.00	9315	▇▁▁▁▁
CWD_MTH04NUMVALID	2002	0.87	36.65	144.13	0	4	11.0	30.00	9704	▇▁▁▁▁
CWD_MTH05NUMVALID	2006	0.87	36.25	143.00	0	4	11.0	30.00	9316	▇▁▁▁▁
CWD_MTH06NUMVALID	2031	0.87	35.06	136.40	0	4	11.0	29.00	8970	▇▁▁▁▁
CWD_MTH07NUMVALID	2384	0.85	34.82	139.01	0	4	11.0	29.00	9421	▇▁▁▁▁
CWD_MTH08NUMVALID	2463	0.84	34.77	129.70	0	4	12.0	30.00	7754	▇▁▁▁▁
CWD_MTHHSNUMVALID	4155	0.74	38.07	144.22	0	5	13.0	31.00	5312	▇▁▁▁▁
ECD_MTH00NUMVALID	405	0.97	834.25	4424.40	0	64	182.0	517.00	293101	▇▁▁▁▁
ECD_MTH03NUMVALID	1792	0.89	142.01	707.64	0	12	33.0	88.00	43322	▇▁▁▁▁
ECD_MTH04NUMVALID	1776	0.89	139.39	691.80	0	12	33.0	87.50	43224	▇▁▁▁▁
ECD_MTH05NUMVALID	1754	0.89	135.33	677.18	0	12	32.0	86.00	43027	▇▁▁▁▁
ECD_MTH06NUMVALID	1772	0.89	131.76	657.63	0	12	32.0	86.00	41090	▇▁▁▁▁
ECD_MTH07NUMVALID	2169	0.86	130.72	663.07	0	13	32.0	85.00	41130	▇▁▁▁▁
ECD_MTH08NUMVALID	2215	0.86	128.30	645.08	0	12	31.0	83.00	41034	▇▁▁▁▁
ECD_MTHHSNUMVALID	3952	0.75	137.66	717.20	0	14	33.0	85.50	40274	▇▁▁▁▁
F_MTH00NUMVALID	47	1.00	797.18	3127.18	0	73	219.0	625.25	174078	▇▁▁▁▁
F_MTH03NUMVALID	1459	0.91	124.90	469.39	0	14	36.0	98.00	25434	▇▁▁▁▁
F_MTH04NUMVALID	1467	0.91	125.04	465.82	0	14	37.0	99.00	25386	▇▁▁▁▁
F_MTH05NUMVALID	1463	0.91	123.30	458.45	0	14	37.0	100.00	25212	▇▁▁▁▁
F_MTH06NUMVALID	1460	0.91	122.60	449.49	0	14	37.0	100.00	23946	▇▁▁▁▁
F_MTH07NUMVALID	1877	0.88	125.05	457.22	0	15	39.0	103.00	24511	▇▁▁▁▁
F_MTH08NUMVALID	1933	0.88	126.89	456.86	0	15	40.0	105.00	24535	▇▁▁▁▁
F_MTHHSNUMVALID	3743	0.76	162.07	640.96	0	19	49.0	124.00	25054	▇▁▁▁▁
HOM_MTH00NUMVALID	9382	0.40	36.95	173.69	0	0	3.0	15.00	5981	▇▁▁▁▁
HOM_MTH03NUMVALID	10803	0.31	8.11	32.35	0	0	1.0	4.00	995	▇▁▁▁▁
HOM_MTH04NUMVALID	10855	0.31	7.72	30.59	0	0	1.0	4.00	817	▇▁▁▁▁
HOM_MTH05NUMVALID	10855	0.31	7.29	29.57	0	0	1.0	4.00	876	▇▁▁▁▁
HOM_MTH06NUMVALID	10923	0.31	6.90	28.44	0	0	1.0	4.00	859	▇▁▁▁▁
HOM_MTH07NUMVALID	11042	0.30	6.58	28.52	0	0	1.0	4.00	907	▇▁▁▁▁
HOM_MTH08NUMVALID	11069	0.30	6.20	27.95	0	0	1.0	3.00	968	▇▁▁▁▁
HOM_MTHHSNUMVALID	11704	0.26	7.04	32.67	0	0	1.0	4.00	1086	▇▁▁▁▁
LEP_MTH00NUMVALID	4294	0.73	188.97	1270.02	0	2	10.0	56.00	95371	▇▁▁▁▁
LEP_MTH03NUMVALID	6354	0.60	52.74	316.59	0	1	4.0	18.00	20260	▇▁▁▁▁
LEP_MTH04NUMVALID	6505	0.59	44.48	268.76	0	1	3.0	15.00	17230	▇▁▁▁▁
LEP_MTH05NUMVALID	6738	0.57	36.13	212.71	0	1	3.0	13.00	14017	▇▁▁▁▁
LEP_MTH06NUMVALID	6940	0.56	30.29	169.92	0	1	3.0	12.00	10875	▇▁▁▁▁
LEP_MTH07NUMVALID	7267	0.54	28.70	167.41	0	1	2.0	11.00	11046	▇▁▁▁▁
LEP_MTH08NUMVALID	7406	0.53	26.58	159.77	0	1	2.0	10.00	10902	▇▁▁▁▁
LEP_MTHHSNUMVALID	8633	0.45	28.18	189.85	0	0	2.0	10.00	11041	▇▁▁▁▁
MAM_MTH00NUMVALID	4167	0.74	27.42	118.62	0	1	3.0	13.00	5454	▇▁▁▁▁
MAM_MTH03NUMVALID	8056	0.49	5.96	21.55	0	0	1.0	4.00	846	▇▁▁▁▁
MAM_MTH04NUMVALID	8050	0.49	5.96	22.13	0	0	1.0	4.00	930	▇▁▁▁▁
MAM_MTH05NUMVALID	8049	0.49	5.94	21.47	0	0	1.0	4.00	842	▇▁▁▁▁
MAM_MTH06NUMVALID	7977	0.49	5.83	20.34	0	0	1.0	4.00	796	▇▁▁▁▁
MAM_MTH07NUMVALID	8141	0.48	5.85	20.12	0	0	1.0	4.00	749	▇▁▁▁▁
MAM_MTH08NUMVALID	8129	0.48	5.74	19.84	0	0	1.0	4.00	738	▇▁▁▁▁
MAM_MTHHSNUMVALID	8926	0.43	6.81	23.86	0	0	1.0	4.00	782	▇▁▁▁▁
MAS_MTH00NUMVALID	3309	0.79	104.60	868.65	0	2	6.0	28.00	74447	▇▁▁▁▁
MAS_MTH03NUMVALID	6732	0.57	20.82	144.82	0	1	2.0	8.00	10783	▇▁▁▁▁
MAS_MTH04NUMVALID	6711	0.57	21.07	148.93	0	1	2.0	8.00	11147	▇▁▁▁▁
MAS_MTH05NUMVALID	6747	0.57	19.93	142.78	0	1	2.0	7.00	10822	▇▁▁▁▁
MAS_MTH06NUMVALID	6816	0.57	20.09	142.97	0	1	2.0	8.00	10584	▇▁▁▁▁
MAS_MTH07NUMVALID	7023	0.55	20.70	143.74	0	1	2.0	8.00	10239	▇▁▁▁▁
MAS_MTH08NUMVALID	7065	0.55	20.97	145.91	0	1	2.0	8.00	10299	▇▁▁▁▁
MAS_MTHHSNUMVALID	7983	0.49	25.96	189.64	0	1	2.0	8.00	10573	▇▁▁▁▁
MBL_MTH00NUMVALID	1951	0.88	296.34	1846.23	0	3	13.0	91.25	87843	▇▁▁▁▁
MBL_MTH03NUMVALID	5389	0.66	56.56	310.58	0	1	4.0	24.00	13822	▇▁▁▁▁
MBL_MTH04NUMVALID	5307	0.66	56.32	300.46	0	1	4.0	24.00	12539	▇▁▁▁▁
MBL_MTH05NUMVALID	5249	0.67	54.86	295.57	0	1	4.0	23.00	12612	▇▁▁▁▁
MBL_MTH06NUMVALID	5239	0.67	54.78	293.46	0	1	4.0	23.25	13169	▇▁▁▁▁
MBL_MTH07NUMVALID	5579	0.65	55.27	294.07	0	1	4.0	25.00	12780	▇▁▁▁▁
MBL_MTH08NUMVALID	5618	0.64	56.25	296.62	0	1	4.0	24.00	12982	▇▁▁▁▁
MBL_MTHHSNUMVALID	6772	0.57	70.33	390.92	0	1	4.0	28.00	14271	▇▁▁▁▁
MHI_MTH00NUMVALID	1348	0.91	407.63	3799.12	0	5	21.0	103.00	262592	▇▁▁▁▁
MHI_MTH03NUMVALID	4108	0.74	76.74	618.54	0	1	5.0	24.00	38320	▇▁▁▁▁
MHI_MTH04NUMVALID	4194	0.73	75.49	621.21	0	1	5.0	23.00	38689	▇▁▁▁▁
MHI_MTH05NUMVALID	4179	0.73	73.55	611.80	0	1	5.0	23.00	38328	▇▁▁▁▁
MHI_MTH06NUMVALID	4231	0.73	72.24	602.96	0	1	5.0	23.00	38729	▇▁▁▁▁
MHI_MTH07NUMVALID	4505	0.71	72.75	620.83	0	1	5.0	22.00	40539	▇▁▁▁▁
MHI_MTH08NUMVALID	4630	0.71	72.23	606.06	0	1	5.0	22.00	37538	▇▁▁▁▁
MHI_MTHHSNUMVALID	6008	0.62	82.21	678.56	0	1	5.0	24.00	37326	▇▁▁▁▁
MIG_MTH00NUMVALID	10326	0.34	24.42	105.06	0	0	1.0	8.00	2574	▇▁▁▁▁
MIG_MTH03NUMVALID	11597	0.26	4.86	18.70	0	0	0.0	2.00	400	▇▁▁▁▁
MIG_MTH04NUMVALID	11613	0.26	4.71	17.98	0	0	0.0	2.00	347	▇▁▁▁▁
MIG_MTH05NUMVALID	11628	0.26	4.67	17.89	0	0	0.0	2.00	356	▇▁▁▁▁
MIG_MTH06NUMVALID	11676	0.26	4.59	17.43	0	0	0.0	2.00	357	▇▁▁▁▁
MIG_MTH07NUMVALID	11814	0.25	4.68	18.08	0	0	0.0	2.00	391	▇▁▁▁▁
MIG_MTH08NUMVALID	11817	0.25	4.59	17.71	0	0	0.0	2.00	381	▇▁▁▁▁
MIG_MTHHSNUMVALID	12362	0.21	5.43	22.77	0	0	0.0	2.00	379	▇▁▁▁▁
MTR_MTH00NUMVALID	9021	0.43	45.37	173.09	0	2	8.0	28.00	6292	▇▁▁▁▁
MTR_MTH03NUMVALID	10440	0.34	9.70	32.82	0	0	2.0	7.00	805	▇▁▁▁▁
MTR_MTH04NUMVALID	10527	0.33	9.11	30.23	0	0	2.0	7.00	880	▇▁▁▁▁
MTR_MTH05NUMVALID	10507	0.33	8.59	29.93	0	0	2.0	6.00	1044	▇▁▁▁▁
MTR_MTH06NUMVALID	10600	0.33	8.31	28.75	0	0	2.0	6.00	1031	▇▁▁▁▁
MTR_MTH07NUMVALID	10776	0.32	8.12	27.96	0	0	2.0	6.00	996	▇▁▁▁▁
MTR_MTH08NUMVALID	10834	0.31	7.86	27.34	0	0	2.0	6.00	975	▇▁▁▁▁
MTR_MTHHSNUMVALID	11647	0.26	9.60	47.00	0	0	1.5	6.00	1894	▇▁▁▁▁
MWH_MTH00NUMVALID	430	0.97	890.47	2126.33	0	88	321.0	907.00	56161	▇▁▁▁▁
MWH_MTH03NUMVALID	1889	0.88	136.04	303.38	0	17	54.0	140.00	8094	▇▁▁▁▁
MWH_MTH04NUMVALID	1889	0.88	137.50	305.12	0	18	55.0	142.00	8120	▇▁▁▁▁
MWH_MTH05NUMVALID	1912	0.88	137.61	301.39	0	18	55.0	143.00	8171	▇▁▁▁▁
MWH_MTH06NUMVALID	1877	0.88	137.28	297.43	0	18	55.0	144.00	8009	▇▁▁▁▁
MWH_MTH07NUMVALID	2251	0.86	140.82	298.64	0	19	58.0	149.00	8077	▇▁▁▁▁
MWH_MTH08NUMVALID	2281	0.86	144.13	304.19	0	20	59.0	152.00	8123	▇▁▁▁▁
MWH_MTHHSNUMVALID	3955	0.75	186.51	516.44	0	26	75.0	183.00	22170	▇▁▁▁▁
M_MTH00NUMVALID	28	1.00	834.15	3254.34	0	77	229.0	661.00	181033	▇▁▁▁▁
M_MTH03NUMVALID	1428	0.91	131.39	494.58	0	14	38.0	104.00	26338	▇▁▁▁▁
M_MTH04NUMVALID	1418	0.91	131.16	487.66	0	14	38.0	104.00	26349	▇▁▁▁▁
M_MTH05NUMVALID	1412	0.91	129.37	479.55	0	14	39.0	104.00	26324	▇▁▁▁▁
M_MTH06NUMVALID	1410	0.91	128.48	471.16	0	14	39.0	105.00	25317	▇▁▁▁▁
M_MTH07NUMVALID	1830	0.88	131.15	479.92	0	15	41.0	108.00	25369	▇▁▁▁▁
M_MTH08NUMVALID	1891	0.88	132.66	475.00	0	16	42.0	111.00	25786	▇▁▁▁▁
M_MTHHSNUMVALID	3712	0.76	165.46	647.22	0	20	51.0	127.00	25550	▇▁▁▁▁
YEAR	0	1.00	2010.00	0.00	2010	2010	2010.0	2010.00	2010	▁▁▇▁▁

Variable type: POSIXct

skim_variable	n_missing	complete_rate	min	max	median	n_unique
DL_INGESTION_DATETIME	0	1	2021-08-20 16:11:49	2021-08-20 16:11:49	2021-08-20 16:11:49	1

skim(rlalea_10)

Data summary

Name	rlalea_10
Number of rows	15717
Number of columns	232
_______________________
Column type frequency:
character	118
numeric	113
POSIXct	1
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
ALL_RLA00PCTPROF	0	1.00	1	5	0	132	0
ALL_RLA03PCTPROF	1339	0.91	2	5	0	123	0
ALL_RLA04PCTPROF	1342	0.91	2	5	0	117	0
ALL_RLA05PCTPROF	1335	0.92	2	5	0	118	0
ALL_RLA06PCTPROF	1333	0.92	2	5	0	116	0
ALL_RLA07PCTPROF	1770	0.89	2	5	0	117	0
ALL_RLA08PCTPROF	1818	0.88	2	5	0	117	0
ALL_RLAHSPCTPROF	3683	0.77	2	5	0	118	0
CWD_RLA00PCTPROF	187	0.99	1	5	0	127	0
CWD_RLA03PCTPROF	1996	0.87	2	5	0	118	0
CWD_RLA04PCTPROF	1986	0.87	1	5	0	109	0
CWD_RLA05PCTPROF	1994	0.87	1	5	0	117	0
CWD_RLA06PCTPROF	2018	0.87	1	5	0	109	0
CWD_RLA07PCTPROF	2374	0.85	1	5	0	102	0
CWD_RLA08PCTPROF	2448	0.84	2	5	0	101	0
CWD_RLAHSPCTPROF	4133	0.74	1	5	0	99	0
ECD_RLA00PCTPROF	401	0.97	1	5	0	123	0
ECD_RLA03PCTPROF	1766	0.89	2	5	0	121	0
ECD_RLA04PCTPROF	1749	0.89	2	5	0	115	0
ECD_RLA05PCTPROF	1731	0.89	2	5	0	114	0
ECD_RLA06PCTPROF	1749	0.89	2	5	0	114	0
ECD_RLA07PCTPROF	2155	0.86	2	5	0	113	0
ECD_RLA08PCTPROF	2198	0.86	2	5	0	120	0
ECD_RLAHSPCTPROF	3966	0.75	2	5	0	119	0
FIPST	0	1.00	2	2	0	51	0
F_RLA00PCTPROF	40	1.00	2	5	0	114	0
F_RLA03PCTPROF	1433	0.91	2	5	0	118	0
F_RLA04PCTPROF	1441	0.91	2	5	0	108	0
F_RLA05PCTPROF	1442	0.91	2	5	0	104	0
F_RLA06PCTPROF	1440	0.91	2	5	0	105	0
F_RLA07PCTPROF	1866	0.88	2	5	0	105	0
F_RLA08PCTPROF	1916	0.88	2	5	0	106	0
F_RLAHSPCTPROF	3752	0.76	2	5	0	108	0
FILEURL	0	1.00	87	87	0	1	0
HOM_RLA00PCTPROF	9576	0.39	2	5	0	94	0
HOM_RLA03PCTPROF	11027	0.30	2	5	0	42	0
HOM_RLA04PCTPROF	11079	0.30	2	5	0	41	0
HOM_RLA05PCTPROF	11078	0.30	2	5	0	40	0
HOM_RLA06PCTPROF	11164	0.29	2	5	0	39	0
HOM_RLA07PCTPROF	11285	0.28	2	5	0	38	0
HOM_RLA08PCTPROF	11324	0.28	2	5	0	40	0
HOM_RLAHSPCTPROF	11942	0.24	2	5	0	44	0
LEAID	0	1.00	7	7	0	15717	0
LEANM09	0	1.00	3	60	0	15436	0
LEP_RLA00PCTPROF	4442	0.72	1	5	0	125	0
LEP_RLA03PCTPROF	6479	0.59	1	5	0	119	0
LEP_RLA04PCTPROF	6656	0.58	1	5	0	109	0
LEP_RLA05PCTPROF	6902	0.56	1	5	0	110	0
LEP_RLA06PCTPROF	7103	0.55	1	5	0	107	0
LEP_RLA07PCTPROF	7462	0.53	1	5	0	99	0
LEP_RLA08PCTPROF	7576	0.52	1	5	0	94	0
LEP_RLAHSPCTPROF	8855	0.44	1	5	0	84	0
MAM_RLA00PCTPROF	4231	0.73	2	5	0	98	0
MAM_RLA03PCTPROF	8186	0.48	2	5	0	43	0
MAM_RLA04PCTPROF	8196	0.48	2	5	0	41	0
MAM_RLA05PCTPROF	8182	0.48	2	5	0	38	0
MAM_RLA06PCTPROF	8120	0.48	2	5	0	41	0
MAM_RLA07PCTPROF	8294	0.47	2	5	0	40	0
MAM_RLA08PCTPROF	8276	0.47	2	5	0	38	0
MAM_RLAHSPCTPROF	9114	0.42	2	5	0	40	0
MAS_RLA00PCTPROF	3360	0.79	2	5	0	96	0
MAS_RLA03PCTPROF	6857	0.56	2	5	0	81	0
MAS_RLA04PCTPROF	6853	0.56	2	5	0	71	0
MAS_RLA05PCTPROF	6889	0.56	2	5	0	74	0
MAS_RLA06PCTPROF	6939	0.56	2	5	0	76	0
MAS_RLA07PCTPROF	7174	0.54	2	5	0	76	0
MAS_RLA08PCTPROF	7188	0.54	2	5	0	74	0
MAS_RLAHSPCTPROF	8130	0.48	2	5	0	80	0
MBL_RLA00PCTPROF	1962	0.88	2	5	0	122	0
MBL_RLA03PCTPROF	5449	0.65	2	5	0	116	0
MBL_RLA04PCTPROF	5362	0.66	2	5	0	112	0
MBL_RLA05PCTPROF	5311	0.66	2	5	0	115	0
MBL_RLA06PCTPROF	5307	0.66	2	5	0	113	0
MBL_RLA07PCTPROF	5649	0.64	2	5	0	113	0
MBL_RLA08PCTPROF	5681	0.64	2	5	0	117	0
MBL_RLAHSPCTPROF	6844	0.56	2	5	0	118	0
MHI_RLA00PCTPROF	1357	0.91	2	5	0	117	0
MHI_RLA03PCTPROF	4127	0.74	2	5	0	120	0
MHI_RLA04PCTPROF	4222	0.73	2	5	0	109	0
MHI_RLA05PCTPROF	4208	0.73	2	5	0	106	0
MHI_RLA06PCTPROF	4278	0.73	2	5	0	107	0
MHI_RLA07PCTPROF	4551	0.71	2	5	0	106	0
MHI_RLA08PCTPROF	4683	0.70	2	5	0	115	0
MHI_RLAHSPCTPROF	6085	0.61	2	5	0	110	0
MIG_RLA00PCTPROF	10414	0.34	2	5	0	76	0
MIG_RLA03PCTPROF	11733	0.25	2	5	0	39	0
MIG_RLA04PCTPROF	11748	0.25	2	5	0	32	0
MIG_RLA05PCTPROF	11765	0.25	2	5	0	33	0
MIG_RLA06PCTPROF	11817	0.25	2	5	0	30	0
MIG_RLA07PCTPROF	11972	0.24	2	5	0	33	0
MIG_RLA08PCTPROF	11969	0.24	2	5	0	35	0
MIG_RLAHSPCTPROF	12556	0.20	2	5	0	38	0
MTR_RLA00PCTPROF	8836	0.44	2	5	0	91	0
MTR_RLA03PCTPROF	10319	0.34	2	5	0	45	0
MTR_RLA04PCTPROF	10417	0.34	2	5	0	39	0
MTR_RLA05PCTPROF	10409	0.34	2	5	0	35	0
MTR_RLA06PCTPROF	10510	0.33	2	5	0	36	0
MTR_RLA07PCTPROF	10696	0.32	2	5	0	40	0
MTR_RLA08PCTPROF	10732	0.32	2	5	0	39	0
MTR_RLAHSPCTPROF	11549	0.27	2	5	0	48	0
MWH_RLA00PCTPROF	421	0.97	2	5	0	109	0
MWH_RLA03PCTPROF	1861	0.88	2	5	0	100	0
MWH_RLA04PCTPROF	1865	0.88	2	5	0	103	0
MWH_RLA05PCTPROF	1895	0.88	2	5	0	98	0
MWH_RLA06PCTPROF	1853	0.88	2	5	0	96	0
MWH_RLA07PCTPROF	2243	0.86	2	5	0	99	0
MWH_RLA08PCTPROF	2264	0.86	2	5	0	103	0
MWH_RLAHSPCTPROF	3967	0.75	2	5	0	101	0
M_RLA00PCTPROF	24	1.00	1	5	0	122	0
M_RLA03PCTPROF	1399	0.91	2	5	0	120	0
M_RLA04PCTPROF	1392	0.91	2	5	0	116	0
M_RLA05PCTPROF	1390	0.91	2	5	0	114	0
M_RLA06PCTPROF	1390	0.91	2	5	0	116	0
M_RLA07PCTPROF	1819	0.88	2	5	0	117	0
M_RLA08PCTPROF	1876	0.88	2	5	0	117	0
M_RLAHSPCTPROF	3725	0.76	2	5	0	116	0
STNAM	0	1.00	4	24	0	51	0
PIPELINE	0	1.00	37	37	0	1	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
ALL_RLA00NUMVALID	0	1.00	1619.22	6341.20	0	150	445.0	1280.00	354300	▇▁▁▁▁
ALL_RLA03NUMVALID	1339	0.91	254.95	959.86	0	28	74.0	200.00	51708	▇▁▁▁▁
ALL_RLA04NUMVALID	1342	0.91	254.90	950.06	0	28	74.0	202.00	51714	▇▁▁▁▁
ALL_RLA05NUMVALID	1335	0.92	251.72	937.02	0	28	75.0	203.00	51447	▇▁▁▁▁
ALL_RLA06NUMVALID	1333	0.92	250.40	919.39	0	28	76.0	205.00	49186	▇▁▁▁▁
ALL_RLA07NUMVALID	1770	0.89	257.32	940.54	0	30	80.0	212.00	49820	▇▁▁▁▁
ALL_RLA08NUMVALID	1818	0.88	258.98	933.11	0	30	81.0	214.00	50401	▇▁▁▁▁
ALL_RLAHSNUMVALID	3683	0.77	308.21	1190.43	0	38	97.0	242.00	50027	▇▁▁▁▁
CWD_RLA00NUMVALID	187	0.99	213.74	882.36	0	21	60.0	174.00	58700	▇▁▁▁▁
CWD_RLA03NUMVALID	1996	0.87	35.38	138.53	0	4	11.0	29.00	9298	▇▁▁▁▁
CWD_RLA04NUMVALID	1986	0.87	36.67	143.98	0	4	11.0	30.00	9681	▇▁▁▁▁
CWD_RLA05NUMVALID	1994	0.87	36.30	142.77	0	4	11.0	30.00	9305	▇▁▁▁▁
CWD_RLA06NUMVALID	2018	0.87	35.11	136.29	0	4	11.0	29.00	8980	▇▁▁▁▁
CWD_RLA07NUMVALID	2374	0.85	34.99	139.13	0	4	11.0	29.00	9404	▇▁▁▁▁
CWD_RLA08NUMVALID	2448	0.84	35.00	130.36	0	4	12.0	30.00	7758	▇▁▁▁▁
CWD_RLAHSNUMVALID	4133	0.74	36.27	134.64	0	5	12.0	30.00	5096	▇▁▁▁▁
ECD_RLA00NUMVALID	401	0.97	829.43	4415.33	0	64	182.0	511.00	292359	▇▁▁▁▁
ECD_RLA03NUMVALID	1766	0.89	141.80	705.93	0	12	33.0	88.00	43268	▇▁▁▁▁
ECD_RLA04NUMVALID	1749	0.89	139.17	690.60	0	12	33.0	87.00	43213	▇▁▁▁▁
ECD_RLA05NUMVALID	1731	0.89	135.21	675.88	0	12	32.0	86.00	42939	▇▁▁▁▁
ECD_RLA06NUMVALID	1749	0.89	131.70	656.48	0	12	32.0	86.00	41010	▇▁▁▁▁
ECD_RLA07NUMVALID	2155	0.86	131.24	662.80	0	13	33.0	86.00	41048	▇▁▁▁▁
ECD_RLA08NUMVALID	2198	0.86	128.42	645.67	0	12	31.0	83.00	41069	▇▁▁▁▁
ECD_RLAHSNUMVALID	3966	0.75	130.61	707.05	0	14	32.0	81.00	39812	▇▁▁▁▁
F_RLA00NUMVALID	40	1.00	791.94	3108.78	0	73	219.0	622.00	173641	▇▁▁▁▁
F_RLA03NUMVALID	1433	0.91	124.80	468.57	0	14	37.0	98.00	25419	▇▁▁▁▁
F_RLA04NUMVALID	1441	0.91	124.92	465.21	0	14	37.0	99.00	25380	▇▁▁▁▁
F_RLA05NUMVALID	1442	0.91	123.40	459.09	0	14	37.0	100.00	25179	▇▁▁▁▁
F_RLA06NUMVALID	1440	0.91	122.83	449.91	0	14	38.0	100.00	23918	▇▁▁▁▁
F_RLA07NUMVALID	1866	0.88	126.15	459.87	0	15	40.0	104.00	24487	▇▁▁▁▁
F_RLA08NUMVALID	1916	0.88	127.14	458.45	0	15	40.0	105.00	24581	▇▁▁▁▁
F_RLAHSNUMVALID	3752	0.76	153.11	594.10	0	19	48.0	120.00	24819	▇▁▁▁▁
HOM_RLA00NUMVALID	9576	0.39	36.96	176.55	0	0	3.0	15.00	5967	▇▁▁▁▁
HOM_RLA03NUMVALID	11027	0.30	8.34	33.04	0	0	1.0	4.00	990	▇▁▁▁▁
HOM_RLA04NUMVALID	11079	0.30	7.92	31.23	0	0	1.0	4.00	811	▇▁▁▁▁
HOM_RLA05NUMVALID	11078	0.30	7.47	30.26	0	0	1.0	4.00	878	▇▁▁▁▁
HOM_RLA06NUMVALID	11164	0.29	7.09	29.13	0	0	1.0	4.00	853	▇▁▁▁▁
HOM_RLA07NUMVALID	11285	0.28	6.80	29.23	0	0	1.0	4.00	906	▇▁▁▁▁
HOM_RLA08NUMVALID	11324	0.28	6.44	28.86	0	0	1.0	3.00	965	▇▁▁▁▁
HOM_RLAHSNUMVALID	11942	0.24	6.82	33.73	0	0	1.0	4.00	1091	▇▁▁▁▁
LEP_RLA00NUMVALID	4442	0.72	188.13	1262.64	0	2	10.0	56.00	94962	▇▁▁▁▁
LEP_RLA03NUMVALID	6479	0.59	53.05	316.84	0	1	4.0	18.00	20195	▇▁▁▁▁
LEP_RLA04NUMVALID	6656	0.58	44.83	269.53	0	1	3.0	16.00	17209	▇▁▁▁▁
LEP_RLA05NUMVALID	6902	0.56	36.41	213.16	0	1	3.0	13.00	13948	▇▁▁▁▁
LEP_RLA06NUMVALID	7103	0.55	30.46	170.11	0	1	3.0	12.00	10814	▇▁▁▁▁
LEP_RLA07NUMVALID	7462	0.53	28.96	167.96	0	1	3.0	11.00	10996	▇▁▁▁▁
LEP_RLA08NUMVALID	7576	0.52	26.81	161.03	0	1	2.0	10.00	10893	▇▁▁▁▁
LEP_RLAHSNUMVALID	8855	0.44	26.85	179.57	0	0	2.0	9.00	10907	▇▁▁▁▁
MAM_RLA00NUMVALID	4231	0.73	27.49	118.97	0	1	4.0	13.00	5481	▇▁▁▁▁
MAM_RLA03NUMVALID	8186	0.48	6.08	21.75	0	0	1.0	4.00	846	▇▁▁▁▁
MAM_RLA04NUMVALID	8196	0.48	6.09	22.35	0	0	1.0	4.00	930	▇▁▁▁▁
MAM_RLA05NUMVALID	8182	0.48	6.07	21.69	0	0	1.0	4.00	843	▇▁▁▁▁
MAM_RLA06NUMVALID	8120	0.48	5.96	20.52	0	0	1.0	4.00	796	▇▁▁▁▁
MAM_RLA07NUMVALID	8294	0.47	6.01	20.35	0	0	1.0	4.00	750	▇▁▁▁▁
MAM_RLA08NUMVALID	8276	0.47	5.89	20.09	0	0	1.0	4.00	738	▇▁▁▁▁
MAM_RLAHSNUMVALID	9114	0.42	6.76	23.98	0	0	1.0	4.00	782	▇▁▁▁▁
MAS_RLA00NUMVALID	3360	0.79	103.96	863.16	0	2	6.0	28.00	74473	▇▁▁▁▁
MAS_RLA03NUMVALID	6857	0.56	21.02	145.69	0	1	2.0	8.00	10786	▇▁▁▁▁
MAS_RLA04NUMVALID	6853	0.56	21.32	150.02	0	1	2.0	8.00	11155	▇▁▁▁▁
MAS_RLA05NUMVALID	6889	0.56	20.27	145.00	0	1	2.0	8.00	10818	▇▁▁▁▁
MAS_RLA06NUMVALID	6939	0.56	20.43	144.87	0	1	2.0	8.00	10588	▇▁▁▁▁
MAS_RLA07NUMVALID	7174	0.54	21.26	146.45	0	1	2.0	8.00	10240	▇▁▁▁▁
MAS_RLA08NUMVALID	7188	0.54	21.32	147.73	0	1	2.0	8.00	10306	▇▁▁▁▁
MAS_RLAHSNUMVALID	8130	0.48	24.73	165.73	0	1	2.0	8.00	10580	▇▁▁▁▁
MBL_RLA00NUMVALID	1962	0.88	293.97	1840.98	0	3	13.0	91.00	87980	▇▁▁▁▁
MBL_RLA03NUMVALID	5449	0.65	57.02	311.92	0	1	4.0	24.00	13865	▇▁▁▁▁
MBL_RLA04NUMVALID	5362	0.66	56.73	301.63	0	1	4.0	24.00	12579	▇▁▁▁▁
MBL_RLA05NUMVALID	5311	0.66	55.35	296.99	0	1	4.0	24.00	12641	▇▁▁▁▁
MBL_RLA06NUMVALID	5307	0.66	55.42	295.19	0	1	4.0	24.00	13179	▇▁▁▁▁
MBL_RLA07NUMVALID	5649	0.64	56.54	297.15	0	1	4.0	25.00	12789	▇▁▁▁▁
MBL_RLA08NUMVALID	5681	0.64	56.73	297.96	0	1	4.0	25.00	13004	▇▁▁▁▁
MBL_RLAHSNUMVALID	6844	0.56	65.27	371.34	0	1	4.0	27.00	14365	▇▁▁▁▁
MHI_RLA00NUMVALID	1357	0.91	406.41	3795.81	0	5	21.0	104.00	261952	▇▁▁▁▁
MHI_RLA03NUMVALID	4127	0.74	76.85	618.33	0	1	5.0	24.00	38267	▇▁▁▁▁
MHI_RLA04NUMVALID	4222	0.73	75.69	621.80	0	1	5.0	23.00	38633	▇▁▁▁▁
MHI_RLA05NUMVALID	4208	0.73	73.77	612.11	0	1	5.0	23.00	38276	▇▁▁▁▁
MHI_RLA06NUMVALID	4278	0.73	72.55	603.84	0	1	5.0	23.00	38702	▇▁▁▁▁
MHI_RLA07NUMVALID	4551	0.71	73.20	621.94	0	1	5.0	23.00	40489	▇▁▁▁▁
MHI_RLA08NUMVALID	4683	0.70	72.85	608.43	0	1	5.0	23.00	37511	▇▁▁▁▁
MHI_RLAHSNUMVALID	6085	0.61	80.49	677.09	0	1	5.0	24.00	36925	▇▁▁▁▁
MIG_RLA00NUMVALID	10414	0.34	24.68	105.48	0	0	1.0	8.00	2570	▇▁▁▁▁
MIG_RLA03NUMVALID	11733	0.25	5.03	18.98	0	0	0.0	2.00	400	▇▁▁▁▁
MIG_RLA04NUMVALID	11748	0.25	4.89	18.28	0	0	0.0	2.00	348	▇▁▁▁▁
MIG_RLA05NUMVALID	11765	0.25	4.85	18.19	0	0	0.0	2.00	356	▇▁▁▁▁
MIG_RLA06NUMVALID	11817	0.25	4.78	17.74	0	0	0.0	2.00	357	▇▁▁▁▁
MIG_RLA07NUMVALID	11972	0.24	4.90	18.45	0	0	0.0	2.00	392	▇▁▁▁▁
MIG_RLA08NUMVALID	11969	0.24	4.80	18.13	0	0	0.0	2.00	379	▇▁▁▁▁
MIG_RLAHSNUMVALID	12556	0.20	5.46	22.90	0	0	0.0	2.00	375	▇▁▁▁▁
MTR_RLA00NUMVALID	8836	0.44	44.81	171.75	0	2	7.0	28.00	6302	▇▁▁▁▁
MTR_RLA03NUMVALID	10319	0.34	9.63	32.58	0	0	2.0	7.00	806	▇▁▁▁▁
MTR_RLA04NUMVALID	10417	0.34	9.07	30.05	0	0	2.0	7.00	880	▇▁▁▁▁
MTR_RLA05NUMVALID	10409	0.34	8.56	29.77	0	0	2.0	6.00	1045	▇▁▁▁▁
MTR_RLA06NUMVALID	10510	0.33	8.29	28.61	0	0	2.0	6.00	1028	▇▁▁▁▁
MTR_RLA07NUMVALID	10696	0.32	8.11	27.93	0	0	2.0	6.00	1005	▇▁▁▁▁
MTR_RLA08NUMVALID	10732	0.32	7.85	27.28	0	0	2.0	6.00	977	▇▁▁▁▁
MTR_RLAHSNUMVALID	11549	0.27	9.55	46.52	0	0	2.0	6.00	1880	▇▁▁▁▁
MWH_RLA00NUMVALID	421	0.97	884.81	2103.53	0	88	319.0	901.25	56235	▇▁▁▁▁
MWH_RLA03NUMVALID	1861	0.88	136.04	303.28	0	17	54.0	140.00	8099	▇▁▁▁▁
MWH_RLA04NUMVALID	1865	0.88	137.51	305.06	0	18	55.0	143.00	8122	▇▁▁▁▁
MWH_RLA05NUMVALID	1895	0.88	137.95	304.15	0	18	55.0	143.00	8173	▇▁▁▁▁
MWH_RLA06NUMVALID	1853	0.88	137.73	299.82	0	18	55.5	145.00	8013	▇▁▁▁▁
MWH_RLA07NUMVALID	2243	0.86	142.37	305.10	0	19	59.0	150.00	8081	▇▁▁▁▁
MWH_RLA08NUMVALID	2264	0.86	144.59	308.40	0	20	59.0	152.00	8171	▇▁▁▁▁
MWH_RLAHSNUMVALID	3967	0.75	175.70	441.50	0	26	73.0	178.00	12213	▇▁▁▁▁
M_RLA00NUMVALID	24	1.00	829.02	3236.33	0	77	229.0	657.00	180650	▇▁▁▁▁
M_RLA03NUMVALID	1399	0.91	131.21	493.41	0	14	38.0	104.00	26286	▇▁▁▁▁
M_RLA04NUMVALID	1392	0.91	131.01	486.85	0	14	38.0	104.00	26333	▇▁▁▁▁
M_RLA05NUMVALID	1390	0.91	129.47	480.15	0	14	39.0	104.00	26267	▇▁▁▁▁
M_RLA06NUMVALID	1390	0.91	128.73	471.69	0	14	39.0	105.00	25267	▇▁▁▁▁
M_RLA07NUMVALID	1819	0.88	132.24	482.73	0	16	41.0	108.00	25333	▇▁▁▁▁
M_RLA08NUMVALID	1876	0.88	133.03	476.86	0	16	42.0	111.00	25819	▇▁▁▁▁
M_RLAHSNUMVALID	3725	0.76	156.44	599.42	0	20	50.0	123.00	25345	▇▁▁▁▁
YEAR	0	1.00	2010.00	0.00	2010	2010	2010.0	2010.00	2010	▁▁▇▁▁

Variable type: POSIXct

skim_variable	n_missing	complete_rate	min	max	median	n_unique
DL_INGESTION_DATETIME	0	1	2021-08-20 16:11:49	2021-08-20 16:11:49	2021-08-20 16:11:49	1

mathlea_10 %>% tabyl(ECD_MTHHSPCTPROF)

##  ECD_MTHHSPCTPROF    n      percent valid_percent
##                10    1 6.350416e-05  8.478169e-05
##             10-14   53 3.365720e-03  4.493429e-03
##             11-19  111 7.048962e-03  9.410767e-03
##                12    1 6.350416e-05  8.478169e-05
##                14    4 2.540166e-04  3.391267e-04
##                15    2 1.270083e-04  1.695634e-04
##             15-19   89 5.651870e-03  7.545570e-03
##                16    1 6.350416e-05  8.478169e-05
##                17    2 1.270083e-04  1.695634e-04
##                18    1 6.350416e-05  8.478169e-05
##                19    8 5.080333e-04  6.782535e-04
##                20    2 1.270083e-04  1.695634e-04
##             20-24  133 8.446053e-03  1.127596e-02
##             20-29  176 1.117673e-02  1.492158e-02
##                21    6 3.810250e-04  5.086901e-04
##             21-39  392 2.489363e-02  3.323442e-02
##                22    6 3.810250e-04  5.086901e-04
##                23    7 4.445291e-04  5.934718e-04
##                24    7 4.445291e-04  5.934718e-04
##                25    6 3.810250e-04  5.086901e-04
##             25-29  175 1.111323e-02  1.483680e-02
##                26   12 7.620499e-04  1.017380e-03
##                27    8 5.080333e-04  6.782535e-04
##                28    8 5.080333e-04  6.782535e-04
##                29    4 2.540166e-04  3.391267e-04
##                 3    1 6.350416e-05  8.478169e-05
##                30   21 1.333587e-03  1.780415e-03
##             30-34  215 1.365339e-02  1.822806e-02
##             30-39  295 1.873373e-02  2.501060e-02
##                31   12 7.620499e-04  1.017380e-03
##                32   12 7.620499e-04  1.017380e-03
##                33   14 8.890582e-04  1.186944e-03
##                34   18 1.143075e-03  1.526070e-03
##                35   15 9.525624e-04  1.271725e-03
##             35-39  202 1.282784e-02  1.712590e-02
##                36   14 8.890582e-04  1.186944e-03
##                37   19 1.206579e-03  1.610852e-03
##                38   20 1.270083e-03  1.695634e-03
##                39   15 9.525624e-04  1.271725e-03
##                40   18 1.143075e-03  1.526070e-03
##             40-44  226 1.435194e-02  1.916066e-02
##             40-49  280 1.778116e-02  2.373887e-02
##             40-59  582 3.695942e-02  4.934294e-02
##                41   24 1.524100e-03  2.034760e-03
##                42   19 1.206579e-03  1.610852e-03
##                43   15 9.525624e-04  1.271725e-03
##                44   12 7.620499e-04  1.017380e-03
##                45   14 8.890582e-04  1.186944e-03
##             45-49  245 1.555852e-02  2.077151e-02
##                46   14 8.890582e-04  1.186944e-03
##                47   10 6.350416e-04  8.478169e-04
##                48   19 1.206579e-03  1.610852e-03
##                49   19 1.206579e-03  1.610852e-03
##                50   14 8.890582e-04  1.186944e-03
##             50-54  215 1.365339e-02  1.822806e-02
##             50-59  317 2.013082e-02  2.687579e-02
##                51   16 1.016067e-03  1.356507e-03
##                52   11 6.985458e-04  9.325986e-04
##                53   17 1.079571e-03  1.441289e-03
##                54   15 9.525624e-04  1.271725e-03
##                55   15 9.525624e-04  1.271725e-03
##             55-59  211 1.339938e-02  1.788894e-02
##                56   19 1.206579e-03  1.610852e-03
##                57   13 8.255541e-04  1.102162e-03
##                58   18 1.143075e-03  1.526070e-03
##                59   19 1.206579e-03  1.610852e-03
##                 6    1 6.350416e-05  8.478169e-05
##               6-9   15 9.525624e-04  1.271725e-03
##                60   18 1.143075e-03  1.526070e-03
##             60-64  250 1.587604e-02  2.119542e-02
##             60-69  358 2.273449e-02  3.035184e-02
##             60-79  658 4.178574e-02  5.578635e-02
##                61    9 5.715374e-04  7.630352e-04
##                62   19 1.206579e-03  1.610852e-03
##                63   18 1.143075e-03  1.526070e-03
##                64   12 7.620499e-04  1.017380e-03
##                65   19 1.206579e-03  1.610852e-03
##             65-69  248 1.574903e-02  2.102586e-02
##                66   12 7.620499e-04  1.017380e-03
##                67   15 9.525624e-04  1.271725e-03
##                68   15 9.525624e-04  1.271725e-03
##                69   12 7.620499e-04  1.017380e-03
##                70   16 1.016067e-03  1.356507e-03
##             70-74  225 1.428844e-02  1.907588e-02
##             70-79  298 1.892424e-02  2.526494e-02
##                71   11 6.985458e-04  9.325986e-04
##                72   14 8.890582e-04  1.186944e-03
##                73    8 5.080333e-04  6.782535e-04
##                74    9 5.715374e-04  7.630352e-04
##                75    9 5.715374e-04  7.630352e-04
##             75-79  168 1.066870e-02  1.424332e-02
##                76    9 5.715374e-04  7.630352e-04
##                77    7 4.445291e-04  5.934718e-04
##                78   11 6.985458e-04  9.325986e-04
##                79    7 4.445291e-04  5.934718e-04
##                 8    1 6.350416e-05  8.478169e-05
##                80    3 1.905125e-04  2.543451e-04
##             80-84  109 6.921953e-03  9.241204e-03
##             80-89  244 1.549501e-02  2.068673e-02
##                81    8 5.080333e-04  6.782535e-04
##                82    5 3.175208e-04  4.239084e-04
##                83    5 3.175208e-04  4.239084e-04
##                84    7 4.445291e-04  5.934718e-04
##                85    3 1.905125e-04  2.543451e-04
##             85-89   93 5.905887e-03  7.884697e-03
##                86    5 3.175208e-04  4.239084e-04
##                87   10 6.350416e-04  8.478169e-04
##                88    8 5.080333e-04  6.782535e-04
##                89   11 6.985458e-04  9.325986e-04
##                 9    1 6.350416e-05  8.478169e-05
##                90    3 1.905125e-04  2.543451e-04
##             90-94   54 3.429225e-03  4.578211e-03
##                91    4 2.540166e-04  3.391267e-04
##                92    4 2.540166e-04  3.391267e-04
##                93    6 3.810250e-04  5.086901e-04
##                94    3 1.905125e-04  2.543451e-04
##                95    2 1.270083e-04  1.695634e-04
##                96    2 1.270083e-04  1.695634e-04
##                97    1 6.350416e-05  8.478169e-05
##                98    1 6.350416e-05  8.478169e-05
##              GE50 1521 9.658983e-02  1.289529e-01
##              GE80  456 2.895790e-02  3.866045e-02
##              GE90  113 7.175970e-03  9.580331e-03
##              GE95   23 1.460596e-03  1.949979e-03
##              LE10   58 3.683241e-03  4.917338e-03
##              LE20  204 1.295485e-02  1.729546e-02
##               LE5   13 8.255541e-04  1.102162e-03
##              LT50  817 5.188290e-02  6.926664e-02
##               n/a   66 4.191275e-03  5.595591e-03
##                PS  975 6.191656e-02  8.266214e-02
##              <NA> 3952 2.509684e-01            NA

mathlea_10 %>% tabyl(LEP_MTHHSPCTPROF)

##  LEP_MTHHSPCTPROF    n      percent valid_percent
##                10    1 6.350416e-05  0.0001405679
##             10-14   59 3.746745e-03  0.0082935058
##                11    3 1.905125e-04  0.0004217037
##             11-19   70 4.445291e-03  0.0098397526
##                12    4 2.540166e-04  0.0005622716
##                13    3 1.905125e-04  0.0004217037
##                14    8 5.080333e-04  0.0011245432
##                15    4 2.540166e-04  0.0005622716
##             15-19   61 3.873754e-03  0.0085746416
##                16    5 3.175208e-04  0.0007028395
##                17    4 2.540166e-04  0.0005622716
##                18    2 1.270083e-04  0.0002811358
##                19    7 4.445291e-04  0.0009839753
##                20    5 3.175208e-04  0.0007028395
##             20-24   49 3.111704e-03  0.0068878268
##             20-29   62 3.937258e-03  0.0087152094
##                21    5 3.175208e-04  0.0007028395
##             21-39  122 7.747507e-03  0.0171492831
##                22    6 3.810250e-04  0.0008434074
##                23    1 6.350416e-05  0.0001405679
##                24    2 1.270083e-04  0.0002811358
##                25    5 3.175208e-04  0.0007028395
##             25-29   40 2.540166e-03  0.0056227158
##                26    6 3.810250e-04  0.0008434074
##                28    6 3.810250e-04  0.0008434074
##                29    2 1.270083e-04  0.0002811358
##             30-34   19 1.206579e-03  0.0026707900
##             30-39   48 3.048200e-03  0.0067472589
##                31    3 1.905125e-04  0.0004217037
##                34    2 1.270083e-04  0.0002811358
##             35-39   26 1.651108e-03  0.0036547653
##                36    4 2.540166e-04  0.0005622716
##                37    1 6.350416e-05  0.0001405679
##                39    2 1.270083e-04  0.0002811358
##                40    1 6.350416e-05  0.0001405679
##             40-44   22 1.397092e-03  0.0030924937
##             40-49   33 2.095637e-03  0.0046387405
##             40-59   90 5.715374e-03  0.0126511105
##                42    1 6.350416e-05  0.0001405679
##                44    1 6.350416e-05  0.0001405679
##                45    3 1.905125e-04  0.0004217037
##             45-49   18 1.143075e-03  0.0025302221
##                46    2 1.270083e-04  0.0002811358
##                48    1 6.350416e-05  0.0001405679
##                49    1 6.350416e-05  0.0001405679
##             50-54   21 1.333587e-03  0.0029519258
##             50-59   31 1.968629e-03  0.0043576047
##                53    1 6.350416e-05  0.0001405679
##             55-59   14 8.890582e-04  0.0019679505
##                 6    1 6.350416e-05  0.0001405679
##               6-9   26 1.651108e-03  0.0036547653
##                60    1 6.350416e-05  0.0001405679
##             60-64    9 5.715374e-04  0.0012651110
##             60-69   15 9.525624e-04  0.0021085184
##             60-79   48 3.048200e-03  0.0067472589
##                63    1 6.350416e-05  0.0001405679
##                65    1 6.350416e-05  0.0001405679
##             65-69    9 5.715374e-04  0.0012651110
##                66    1 6.350416e-05  0.0001405679
##                 7    1 6.350416e-05  0.0001405679
##             70-74   11 6.985458e-04  0.0015462468
##             70-79   16 1.016067e-03  0.0022490863
##                71    2 1.270083e-04  0.0002811358
##             75-79    5 3.175208e-04  0.0007028395
##                 8    1 6.350416e-05  0.0001405679
##                80    1 6.350416e-05  0.0001405679
##             80-84    5 3.175208e-04  0.0007028395
##             80-89    4 2.540166e-04  0.0005622716
##                82    1 6.350416e-05  0.0001405679
##                83    1 6.350416e-05  0.0001405679
##                84    1 6.350416e-05  0.0001405679
##                85    1 6.350416e-05  0.0001405679
##             85-89    6 3.810250e-04  0.0008434074
##                87    3 1.905125e-04  0.0004217037
##                 9    6 3.810250e-04  0.0008434074
##             90-94    6 3.810250e-04  0.0008434074
##              GE50  330 2.095637e-02  0.0463874051
##              GE80   34 2.159141e-03  0.0047793084
##              GE90    3 1.905125e-04  0.0004217037
##              LE10   68 4.318283e-03  0.0095586168
##              LE20  171 1.085921e-02  0.0240371099
##               LE5   17 1.079571e-03  0.0023896542
##              LT50  662 4.203975e-02  0.0930559460
##               n/a 1850 1.174827e-01  0.2600506044
##                PS 2909 1.847336e-01  0.4089120045
##              <NA> 8633 5.482314e-01            NA

mathlea_10 %>% tabyl(HOM_MTHHSPCTPROF)

##  HOM_MTHHSPCTPROF     n      percent valid_percent
##                10     1 6.350416e-05  0.0002473411
##             10-14     1 6.350416e-05  0.0002473411
##             11-19     8 5.080333e-04  0.0019787287
##             15-19     2 1.270083e-04  0.0004946822
##                19     1 6.350416e-05  0.0002473411
##                 2     1 6.350416e-05  0.0002473411
##             20-24     2 1.270083e-04  0.0004946822
##             20-29    12 7.620499e-04  0.0029680930
##             21-39    49 3.111704e-03  0.0121197131
##             25-29     5 3.175208e-04  0.0012367054
##                28     1 6.350416e-05  0.0002473411
##             30-34    12 7.620499e-04  0.0029680930
##             30-39    17 1.079571e-03  0.0042047984
##             35-39     6 3.810250e-04  0.0014840465
##                39     1 6.350416e-05  0.0002473411
##             40-44    10 6.350416e-04  0.0024734108
##             40-49    13 8.255541e-04  0.0032154341
##             40-59    52 3.302216e-03  0.0128617363
##                43     1 6.350416e-05  0.0002473411
##                44     1 6.350416e-05  0.0002473411
##             45-49    13 8.255541e-04  0.0032154341
##                49     1 6.350416e-05  0.0002473411
##             50-54     8 5.080333e-04  0.0019787287
##             50-59    15 9.525624e-04  0.0037101163
##                53     1 6.350416e-05  0.0002473411
##             55-59     3 1.905125e-04  0.0007420233
##               6-9     1 6.350416e-05  0.0002473411
##             60-64     3 1.905125e-04  0.0007420233
##             60-69     6 3.810250e-04  0.0014840465
##             60-79    35 2.222646e-03  0.0086569379
##             65-69     3 1.905125e-04  0.0007420233
##             70-74     2 1.270083e-04  0.0004946822
##             70-79    11 6.985458e-04  0.0027207519
##             75-79     1 6.350416e-05  0.0002473411
##             80-84     2 1.270083e-04  0.0004946822
##             80-89     5 3.175208e-04  0.0012367054
##             90-94     1 6.350416e-05  0.0002473411
##              GE50   191 1.212929e-02  0.0472421469
##              GE80     5 3.175208e-04  0.0012367054
##              GE90     2 1.270083e-04  0.0004946822
##              LE10     4 2.540166e-04  0.0009893643
##              LE20    25 1.587604e-03  0.0061835271
##               LE5     1 6.350416e-05  0.0002473411
##              LT50   241 1.530450e-02  0.0596092011
##               n/a  1819 1.155141e-01  0.4499134306
##                PS  1448 9.195402e-02  0.3581498887
##              <NA> 11704 7.432527e-01            NA

mathlea_10 %>% tabyl(MIG_MTHHSPCTPROF)

##  MIG_MTHHSPCTPROF     n      percent valid_percent
##             10-14     1 6.350416e-05  0.0002954210
##             11-19     6 3.810250e-04  0.0017725258
##             15-19     5 3.175208e-04  0.0014771049
##             20-24     3 1.905125e-04  0.0008862629
##             20-29    11 6.985458e-04  0.0032496307
##             21-39    29 1.841621e-03  0.0085672083
##             25-29    10 6.350416e-04  0.0029542097
##             30-34     4 2.540166e-04  0.0011816839
##             30-39    22 1.397092e-03  0.0064992614
##                34     1 6.350416e-05  0.0002954210
##             35-39    11 6.985458e-04  0.0032496307
##                38     1 6.350416e-05  0.0002954210
##             40-44     7 4.445291e-04  0.0020679468
##             40-49    12 7.620499e-04  0.0035450517
##             40-59    25 1.587604e-03  0.0073855244
##                41     2 1.270083e-04  0.0005908419
##             45-49     4 2.540166e-04  0.0011816839
##             50-54     2 1.270083e-04  0.0005908419
##             50-59     9 5.715374e-04  0.0026587888
##             55-59     5 3.175208e-04  0.0014771049
##                58     1 6.350416e-05  0.0002954210
##               6-9     1 6.350416e-05  0.0002954210
##             60-64     5 3.175208e-04  0.0014771049
##             60-69     5 3.175208e-04  0.0014771049
##             60-79    15 9.525624e-04  0.0044313146
##             65-69     5 3.175208e-04  0.0014771049
##             70-74     2 1.270083e-04  0.0005908419
##             70-79     2 1.270083e-04  0.0005908419
##             75-79     1 6.350416e-05  0.0002954210
##             80-84     1 6.350416e-05  0.0002954210
##             80-89     1 6.350416e-05  0.0002954210
##              GE50   102 6.477424e-03  0.0301329394
##              GE80     6 3.810250e-04  0.0017725258
##              LE10     3 1.905125e-04  0.0008862629
##              LE20    13 8.255541e-04  0.0038404727
##              LT50   141 8.954086e-03  0.0416543575
##               n/a  1963 1.246587e-01  0.5799113737
##                PS   948 6.020194e-02  0.2800590842
##              <NA> 12362 7.850384e-01            NA

mathlea_10 %>% tabyl(CWD_MTHHSPCTPROF)

##  CWD_MTHHSPCTPROF    n      percent valid_percent
##                10    2 1.270083e-04  1.725328e-04
##             10-14  131 8.319045e-03  1.130090e-02
##                11   11 6.985458e-04  9.489303e-04
##             11-19  250 1.587604e-02  2.156660e-02
##                12    3 1.905125e-04  2.587992e-04
##                13    3 1.905125e-04  2.587992e-04
##                14    2 1.270083e-04  1.725328e-04
##                15    5 3.175208e-04  4.313320e-04
##             15-19  153 9.716136e-03  1.319876e-02
##                16    5 3.175208e-04  4.313320e-04
##                17    3 1.905125e-04  2.587992e-04
##                18    2 1.270083e-04  1.725328e-04
##                19    6 3.810250e-04  5.175983e-04
##                 2    1 6.350416e-05  8.626639e-05
##                20    3 1.905125e-04  2.587992e-04
##             20-24  128 8.128532e-03  1.104210e-02
##             20-29  267 1.695561e-02  2.303313e-02
##                21    8 5.080333e-04  6.901311e-04
##             21-39  624 3.962660e-02  5.383023e-02
##                22    6 3.810250e-04  5.175983e-04
##                24    2 1.270083e-04  1.725328e-04
##                25    9 5.715374e-04  7.763975e-04
##             25-29  116 7.366483e-03  1.000690e-02
##                26    4 2.540166e-04  3.450656e-04
##                27    2 1.270083e-04  1.725328e-04
##                28    4 2.540166e-04  3.450656e-04
##                29    1 6.350416e-05  8.626639e-05
##                30    4 2.540166e-04  3.450656e-04
##             30-34  111 7.048962e-03  9.575569e-03
##             30-39  243 1.543151e-02  2.096273e-02
##                32    5 3.175208e-04  4.313320e-04
##                33    3 1.905125e-04  2.587992e-04
##                34    7 4.445291e-04  6.038647e-04
##                35    1 6.350416e-05  8.626639e-05
##             35-39   81 5.143837e-03  6.987578e-03
##                36    3 1.905125e-04  2.587992e-04
##                37    3 1.905125e-04  2.587992e-04
##                38    2 1.270083e-04  1.725328e-04
##                39    1 6.350416e-05  8.626639e-05
##                40    5 3.175208e-04  4.313320e-04
##             40-44   93 5.905887e-03  8.022774e-03
##             40-49  177 1.124024e-02  1.526915e-02
##             40-59  445 2.825935e-02  3.838854e-02
##                41    7 4.445291e-04  6.038647e-04
##                42    1 6.350416e-05  8.626639e-05
##                43    2 1.270083e-04  1.725328e-04
##                44    4 2.540166e-04  3.450656e-04
##                45    3 1.905125e-04  2.587992e-04
##             45-49   74 4.699308e-03  6.383713e-03
##                46    1 6.350416e-05  8.626639e-05
##                47    2 1.270083e-04  1.725328e-04
##                48    1 6.350416e-05  8.626639e-05
##                49    4 2.540166e-04  3.450656e-04
##                50    2 1.270083e-04  1.725328e-04
##             50-54   51 3.238712e-03  4.399586e-03
##             50-59  129 8.192037e-03  1.112836e-02
##                51    2 1.270083e-04  1.725328e-04
##                52    1 6.350416e-05  8.626639e-05
##                53    2 1.270083e-04  1.725328e-04
##                54    1 6.350416e-05  8.626639e-05
##                55    2 1.270083e-04  1.725328e-04
##             55-59   47 2.984695e-03  4.054520e-03
##                57    2 1.270083e-04  1.725328e-04
##                58    1 6.350416e-05  8.626639e-05
##                59    1 6.350416e-05  8.626639e-05
##               6-9   66 4.191275e-03  5.693582e-03
##                60    1 6.350416e-05  8.626639e-05
##             60-64   37 2.349654e-03  3.191856e-03
##             60-69  113 7.175970e-03  9.748102e-03
##             60-79  255 1.619356e-02  2.199793e-02
##                61    1 6.350416e-05  8.626639e-05
##                62    2 1.270083e-04  1.725328e-04
##                63    1 6.350416e-05  8.626639e-05
##                65    2 1.270083e-04  1.725328e-04
##             65-69   39 2.476662e-03  3.364389e-03
##                66    1 6.350416e-05  8.626639e-05
##                67    1 6.350416e-05  8.626639e-05
##                69    2 1.270083e-04  1.725328e-04
##                 7    1 6.350416e-05  8.626639e-05
##             70-74   25 1.587604e-03  2.156660e-03
##             70-79   71 4.508795e-03  6.124914e-03
##                73    1 6.350416e-05  8.626639e-05
##                75    1 6.350416e-05  8.626639e-05
##             75-79   27 1.714612e-03  2.329193e-03
##                76    1 6.350416e-05  8.626639e-05
##                77    2 1.270083e-04  1.725328e-04
##                78    2 1.270083e-04  1.725328e-04
##                80    1 6.350416e-05  8.626639e-05
##             80-84   20 1.270083e-03  1.725328e-03
##             80-89   47 2.984695e-03  4.054520e-03
##                81    2 1.270083e-04  1.725328e-04
##                82    1 6.350416e-05  8.626639e-05
##                84    1 6.350416e-05  8.626639e-05
##             85-89   15 9.525624e-04  1.293996e-03
##                86    1 6.350416e-05  8.626639e-05
##                87    1 6.350416e-05  8.626639e-05
##                 9    1 6.350416e-05  8.626639e-05
##             90-94   11 6.985458e-04  9.489303e-04
##              GE50 1028 6.528228e-02  8.868185e-02
##              GE80   96 6.096399e-03  8.281573e-03
##              GE90   27 1.714612e-03  2.329193e-03
##              GE95    1 6.350416e-05  8.626639e-05
##              LE10  206 1.308186e-02  1.777088e-02
##              LE20  750 4.762812e-02  6.469979e-02
##               LE5   27 1.714612e-03  2.329193e-03
##              LT50 2342 1.487267e-01  2.020359e-01
##               n/a  165 1.047819e-02  1.423395e-02
##                PS 2922 1.855592e-01  2.520704e-01
##              <NA> 4155 2.638598e-01            NA

Data Visualization 2

Ksenia

Research Question:

What drives current expenditure on education? How is funding allocated by state and how do the funding allocations correlate with eh student performance (graduation rates and/or test scores)? Does cross-state variation in expenditure explain the cross-state variation in education outcomes? More specifically, I might look at the following:

• What percentage of expenditure accounts for instruction/ textbooks/ technology-related equipment & services/ instructional equipment?
• What are the gross amounts spent on those categories?
• How do those percentages/ gross amounts correlate with the diversity of the student population/portion of white students in school?
• Does increased spending on any of the categories correlate with increased students’ performance?
  
• Can higher teachers’ salaries result in better test scores?
• What amount of funding is spent on special education programs and how that correlates with performance of students with disabilities?

Datasets:

• EDFacts_rla_achievement_lea_2010_2019
  
• EDFacts_math_achievement_lea_2010_2019
  
• NCES_CCD_fiscal_district_2010

colnames(fiscal2010)

##   [1] "A07"                   "A08"                   "A09"                  
##   [4] "A11"                   "A13"                   "A15"                  
##   [7] "A20"                   "A40"                   "AGCHRT"               
##  [10] "B10"                   "B11"                   "B12"                  
##  [13] "B13"                   "C01"                   "C04"                  
##  [16] "C05"                   "C06"                   "C07"                  
##  [19] "C08"                   "C09"                   "C10"                  
##  [22] "C11"                   "C12"                   "C13"                  
##  [25] "C14"                   "C15"                   "C16"                  
##  [28] "C17"                   "C19"                   "C20"                  
##  [31] "C24"                   "C25"                   "C35"                  
##  [34] "C36"                   "C38"                   "C39"                  
##  [37] "CBSA"                  "CCDNF"                 "CENFILE"              
##  [40] "CENSUSID"              "CONUM"                 "CSA"                  
##  [43] "D11"                   "D23"                   "E07"                  
##  [46] "E08"                   "E09"                   "E11"                  
##  [49] "E13"                   "E17"                   "F12"                  
##  [52] "FIPST"                 "FL_19H"                "FL_21F"               
##  [55] "FL_31F"                "FL_41F"                "FL_61V"               
##  [58] "FL_66V"                "FL_A07"                "FL_A08"               
##  [61] "FL_A09"                "FL_A11"                "FL_A13"               
##  [64] "FL_A15"                "FL_A20"                "FL_A40"               
##  [67] "FL_B10"                "FL_B11"                "FL_B12"               
##  [70] "FL_B13"                "FL_C01"                "FL_C04"               
##  [73] "FL_C05"                "FL_C06"                "FL_C07"               
##  [76] "FL_C08"                "FL_C09"                "FL_C10"               
##  [79] "FL_C11"                "FL_C12"                "FL_C13"               
##  [82] "FL_C14"                "FL_C15"                "FL_C16"               
##  [85] "FL_C17"                "FL_C19"                "FL_C20"               
##  [88] "FL_C24"                "FL_C25"                "FL_C35"               
##  [91] "FL_C36"                "FL_C38"                "FL_C39"               
##  [94] "FL_D11"                "FL_D23"                "FL_E07"               
##  [97] "FL_E08"                "FL_E09"                "FL_E11"               
## [100] "FL_E13"                "FL_E17"                "FL_F12"               
## [103] "FL_G15"                "FL_HE1"                "FL_HE2"               
## [106] "FL_HR1"                "FL_I86"                "FL_K09"               
## [109] "FL_K10"                "FL_K11"                "FL_L12"               
## [112] "FL_M12"                "FL_MEMBERSCH"          "FL_Q11"               
## [115] "FL_T02"                "FL_T06"                "FL_T09"               
## [118] "FL_T15"                "FL_T40"                "FL_T99"               
## [121] "FL_U11"                "FL_U22"                "FL_U30"               
## [124] "FL_U50"                "FL_U97"                "FL_V10"               
## [127] "FL_V11"                "FL_V12"                "FL_V13"               
## [130] "FL_V14"                "FL_V15"                "FL_V16"               
## [133] "FL_V17"                "FL_V18"                "FL_V21"               
## [136] "FL_V22"                "FL_V23"                "FL_V24"               
## [139] "FL_V29"                "FL_V30"                "FL_V32"               
## [142] "FL_V33"                "FL_V37"                "FL_V38"               
## [145] "FL_V40"                "FL_V45"                "FL_V60"               
## [148] "FL_V65"                "FL_V70"                "FL_V75"               
## [151] "FL_V80"                "FL_V85"                "FL_V90"               
## [154] "FL_V91"                "FL_V92"                "FL_V93"               
## [157] "FL_W01"                "FL_W31"                "FL_W61"               
## [160] "FL_Z32"                "FL_Z33"                "FL_Z34"               
## [163] "FL_Z35"                "FL_Z36"                "FL_Z37"               
## [166] "FL_Z38"                "FILEURL"               "G15"                  
## [169] "GSHI"                  "GSLO"                  "HE1"                  
## [172] "HE2"                   "HR1"                   "I86"                  
## [175] "K09"                   "K10"                   "K11"                  
## [178] "L12"                   "LEAID"                 "M12"                  
## [181] "MEMBERSCH"             "NAME"                  "Q11"                  
## [184] "SCHLEV"                "STABBR"                "STNAME"               
## [187] "T02"                   "T06"                   "T09"                  
## [190] "T15"                   "T40"                   "T99"                  
## [193] "TCAPOUT"               "TCURELSC"              "TCURINST"             
## [196] "TCUROTH"               "TCURSSVC"              "TFEDREV"              
## [199] "TLOCREV"               "TNONELSE"              "TOTALEXP"             
## [202] "TOTALREV"              "TSTREV"                "U11"                  
## [205] "U22"                   "U30"                   "U50"                  
## [208] "U97"                   "V10"                   "V11"                  
## [211] "V12"                   "V13"                   "V14"                  
## [214] "V15"                   "V16"                   "V17"                  
## [217] "V18"                   "V21"                   "V22"                  
## [220] "V23"                   "V24"                   "V29"                  
## [223] "V30"                   "V32"                   "V33"                  
## [226] "V37"                   "V38"                   "V40"                  
## [229] "V45"                   "V60"                   "V65"                  
## [232] "V70"                   "V75"                   "V80"                  
## [235] "V85"                   "V90"                   "V91"                  
## [238] "V92"                   "V93"                   "W01"                  
## [241] "W31"                   "W61"                   "WEIGHT"               
## [244] "Z32"                   "Z33"                   "Z34"                  
## [247] "Z35"                   "Z36"                   "Z37"                  
## [250] "Z38"                   "_19H"                  "_21F"                 
## [253] "_31F"                  "_41F"                  "_61V"                 
## [256] "_66V"                  "_YEAR"                 "YEAR"                 
## [259] "PIPELINE"              "DL_INGESTION_DATETIME"

skim(fiscal2010)

Data summary

Name	fiscal2010
Number of rows	18247
Number of columns	260
_______________________
Column type frequency:
character	129
numeric	130
POSIXct	1
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
AGCHRT	0	1	1	1	0	4	0
CBSA	0	1	1	5	0	939	0
CENSUSID	0	1	1	14	0	14842	0
CONUM	0	1	5	5	0	3129	0
CSA	0	1	1	3	0	125	0
FIPST	0	1	2	2	0	51	0
FL_19H	0	1	1	1	0	4	0
FL_21F	0	1	1	1	0	4	0
FL_31F	0	1	1	1	0	4	0
FL_41F	0	1	1	1	0	4	0
FL_61V	0	1	1	1	0	3	0
FL_66V	0	1	1	1	0	4	0
FL_A07	0	1	1	1	0	4	0
FL_A08	0	1	1	1	0	3	0
FL_A09	0	1	1	1	0	3	0
FL_A11	0	1	1	1	0	3	0
FL_A13	0	1	1	1	0	3	0
FL_A15	0	1	1	1	0	4	0
FL_A20	0	1	1	1	0	4	0
FL_A40	0	1	1	1	0	4	0
FL_B10	0	1	1	1	0	3	0
FL_B11	0	1	1	1	0	3	0
FL_B12	0	1	1	1	0	4	0
FL_B13	0	1	1	1	0	3	0
FL_C01	0	1	1	1	0	4	0
FL_C04	0	1	1	1	0	4	0
FL_C05	0	1	1	1	0	3	0
FL_C06	0	1	1	1	0	3	0
FL_C07	0	1	1	1	0	4	0
FL_C08	0	1	1	1	0	3	0
FL_C09	0	1	1	1	0	4	0
FL_C10	0	1	1	1	0	3	0
FL_C11	0	1	1	1	0	3	0
FL_C12	0	1	1	1	0	3	0
FL_C13	0	1	1	1	0	4	0
FL_C14	0	1	1	1	0	4	0
FL_C15	0	1	1	1	0	3	0
FL_C16	0	1	1	1	0	3	0
FL_C17	0	1	1	1	0	3	0
FL_C19	0	1	1	1	0	3	0
FL_C20	0	1	1	1	0	4	0
FL_C24	0	1	1	1	0	3	0
FL_C25	0	1	1	1	0	4	0
FL_C35	0	1	1	1	0	3	0
FL_C36	0	1	1	1	0	4	0
FL_C38	0	1	1	1	0	4	0
FL_C39	0	1	1	1	0	4	0
FL_D11	0	1	1	1	0	4	0
FL_D23	0	1	1	1	0	4	0
FL_E07	0	1	1	1	0	5	0
FL_E08	0	1	1	1	0	5	0
FL_E09	0	1	1	1	0	5	0
FL_E11	0	1	1	1	0	4	0
FL_E13	0	1	1	1	0	5	0
FL_E17	0	1	1	1	0	5	0
FL_F12	0	1	1	1	0	4	0
FL_G15	0	1	1	1	0	4	0
FL_HE1	0	1	1	1	0	4	0
FL_HE2	0	1	1	1	0	4	0
FL_HR1	0	1	1	1	0	4	0
FL_I86	0	1	1	1	0	4	0
FL_K09	0	1	1	1	0	5	0
FL_K10	0	1	1	1	0	4	0
FL_K11	0	1	1	1	0	4	0
FL_L12	0	1	1	1	0	3	0
FL_M12	0	1	1	1	0	3	0
FL_MEMBERSCH	0	1	1	1	0	3	0
FL_Q11	0	1	1	1	0	4	0
FL_T02	0	1	1	1	0	4	0
FL_T06	0	1	1	1	0	4	0
FL_T09	0	1	1	1	0	3	0
FL_T15	0	1	1	1	0	3	0
FL_T40	0	1	1	1	0	3	0
FL_T99	0	1	1	1	0	4	0
FL_U11	0	1	1	1	0	3	0
FL_U22	0	1	1	1	0	4	0
FL_U30	0	1	1	1	0	3	0
FL_U50	0	1	1	1	0	4	0
FL_U97	0	1	1	1	0	4	0
FL_V10	0	1	1	1	0	5	0
FL_V11	0	1	1	1	0	4	0
FL_V12	0	1	1	1	0	4	0
FL_V13	0	1	1	1	0	3	0
FL_V14	0	1	1	1	0	4	0
FL_V15	0	1	1	1	0	3	0
FL_V16	0	1	1	1	0	4	0
FL_V17	0	1	1	1	0	3	0
FL_V18	0	1	1	1	0	4	0
FL_V21	0	1	1	1	0	3	0
FL_V22	0	1	1	1	0	4	0
FL_V23	0	1	1	1	0	4	0
FL_V24	0	1	1	1	0	5	0
FL_V29	0	1	1	1	0	3	0
FL_V30	0	1	1	1	0	5	0
FL_V32	0	1	1	1	0	3	0
FL_V33	0	1	1	1	0	3	0
FL_V37	0	1	1	1	0	4	0
FL_V38	0	1	1	1	0	5	0
FL_V40	0	1	1	1	0	4	0
FL_V45	0	1	1	1	0	5	0
FL_V60	0	1	1	1	0	4	0
FL_V65	0	1	1	1	0	4	0
FL_V70	0	1	1	1	0	3	0
FL_V75	0	1	1	1	0	3	0
FL_V80	0	1	1	1	0	4	0
FL_V85	0	1	1	1	0	4	0
FL_V90	0	1	1	1	0	5	0
FL_V91	0	1	1	1	0	4	0
FL_V92	0	1	1	1	0	3	0
FL_V93	0	1	1	1	0	4	0
FL_W01	0	1	1	1	0	4	0
FL_W31	0	1	1	1	0	4	0
FL_W61	0	1	1	1	0	4	0
FL_Z32	0	1	1	1	0	4	0
FL_Z33	0	1	1	1	0	4	0
FL_Z34	0	1	1	1	0	5	0
FL_Z35	0	1	1	1	0	4	0
FL_Z36	0	1	1	1	0	3	0
FL_Z37	0	1	1	1	0	4	0
FL_Z38	0	1	1	1	0	3	0
FILEURL	0	1	48	48	0	1	0
GSHI	0	1	1	2	0	16	0
GSLO	0	1	1	2	0	16	0
LEAID	0	1	7	7	0	18247	0
NAME	0	1	3	60	0	17717	0
SCHLEV	0	1	1	2	0	7	0
STABBR	0	1	2	2	0	51	0
STNAME	0	1	4	20	0	51	0
PIPELINE	0	1	29	29	0	1	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
A07	0	1	62321.43	402543.33	-2	0	0	9000.0	20229000	▇▁▁▁▁
A08	0	1	5939.67	128277.93	-2	0	0	0.0	15564000	▇▁▁▁▁
A09	0	1	364123.59	1144107.53	-2	3000	79000	319000.0	47205000	▇▁▁▁▁
A11	0	1	10957.26	85439.61	-2	0	0	0.0	3510000	▇▁▁▁▁
A13	0	1	211909.26	1823605.27	-2	0	8000	97000.0	171949000	▇▁▁▁▁
A15	0	1	8501.85	191879.04	-2	0	0	0.0	14939000	▇▁▁▁▁
A20	0	1	91805.46	808818.77	-2	0	0	3000.0	49072000	▇▁▁▁▁
A40	0	1	41828.48	293969.27	-2	0	0	9000.0	19348000	▇▁▁▁▁
B10	0	1	68834.01	917277.92	-2	0	0	0.0	59398000	▇▁▁▁▁
B11	0	1	19931.51	259028.50	-2	0	0	0.0	28169000	▇▁▁▁▁
B12	0	1	5454.66	56806.67	-2	0	0	0.0	3111000	▇▁▁▁▁
B13	0	1	162690.86	2006666.21	-2	0	0	24000.0	213069000	▇▁▁▁▁
C01	0	1	9904463.16	56726682.92	-2	481000	2407000	7717500.0	6016802000	▇▁▁▁▁
C04	0	1	177204.10	1791292.67	-2	0	0	0.0	139157000	▇▁▁▁▁
C05	0	1	918167.93	12489751.14	-2	0	0	331000.0	1445472000	▇▁▁▁▁
C06	0	1	255034.54	3212965.61	-2	0	0	47000.0	262095000	▇▁▁▁▁
C07	0	1	33335.24	848165.31	-2	0	0	0.0	52887000	▇▁▁▁▁
C08	0	1	23623.02	667675.15	-2	0	0	0.0	68096000	▇▁▁▁▁
C09	0	1	43071.09	351589.50	-2	0	0	0.0	16140000	▇▁▁▁▁
C10	0	1	28732.95	385107.93	-2	0	2000	12000.0	42871000	▇▁▁▁▁
C11	0	1	340673.71	3307919.59	-2	0	0	0.0	289060000	▇▁▁▁▁
C12	0	1	219562.73	1235888.02	-2	0	0	68000.0	76286000	▇▁▁▁▁
C13	0	1	1467624.72	15568842.66	-2	1000	76000	506000.0	1684314000	▇▁▁▁▁
C14	0	1	928407.42	8605443.22	-2	23000	146000	508000.0	777993000	▇▁▁▁▁
C15	0	1	783204.43	4229045.40	-2	0	69000	473000.0	257678000	▇▁▁▁▁
C16	0	1	91089.01	662844.07	-2	0	0	43000.0	58822000	▇▁▁▁▁
C17	0	1	13732.90	128064.08	-2	0	0	4500.0	10177000	▇▁▁▁▁
C19	0	1	34663.02	223558.75	-2	0	0	2000.0	15750000	▇▁▁▁▁
C20	0	1	1216737.06	6249021.27	-2	1000	178000	753000.0	394540000	▇▁▁▁▁
C24	0	1	198884.54	5846320.83	-2	0	0	0.0	729737000	▇▁▁▁▁
C25	0	1	658136.53	4121046.74	-2	11000	118000	399500.0	321606000	▇▁▁▁▁
C35	0	1	111686.04	1947391.23	-2	0	0	0.0	208110000	▇▁▁▁▁
C36	0	1	141945.32	4773984.42	-2	0	0	0.0	637078000	▇▁▁▁▁
C38	0	1	631631.02	3267240.10	-2	0	0	161000.0	144110000	▇▁▁▁▁
C39	0	1	31920.66	320989.51	-2	0	0	0.0	19306000	▇▁▁▁▁
CCDNF	0	1	1.00	0.04	0	1	1	1.0	1	▁▁▁▁▇
CENFILE	0	1	0.81	0.39	0	1	1	1.0	1	▂▁▁▁▇
D11	0	1	603561.64	3194163.22	-2	0	0	208000.0	201357000	▇▁▁▁▁
D23	0	1	442007.85	3606971.99	-2	0	0	46000.0	340982000	▇▁▁▁▁
E07	0	1	1366785.35	6893020.79	-2	34000	227000	952000.0	479925000	▇▁▁▁▁
E08	0	1	550224.54	1818703.71	-2	101000	283000	593000.0	144467000	▇▁▁▁▁
E09	0	1	1559184.15	7539644.20	-2	102000	381000	1206000.0	528651000	▇▁▁▁▁
E11	0	1	1071170.56	5492398.66	-2	50000	261000	830000.0	448671000	▇▁▁▁▁
E13	0	1	17327081.34	127893156.30	-2	1171000	4198000	13455500.0	14936045000	▇▁▁▁▁
E17	0	1	1578756.96	6042710.01	-2	46000	286000	1203000.0	333309000	▇▁▁▁▁
F12	0	1	2583185.58	27822230.17	-2	0	37000	675000.0	3044559000	▇▁▁▁▁
G15	0	1	181778.44	1733083.39	-2	0	0	0.0	106072000	▇▁▁▁▁
HE1	0	1	1388857.91	10400685.61	-2	55000	289000	968000.0	1050214000	▇▁▁▁▁
HE2	0	1	64137.90	490090.60	-2	0	0	24000.0	36903000	▇▁▁▁▁
HR1	0	1	218646.58	2227187.59	-2	0	26000	115000.0	232282000	▇▁▁▁▁
I86	0	1	971108.03	6437069.74	-2	0	64000	529000.0	439446000	▇▁▁▁▁
K09	0	1	137397.72	665773.23	-2	0	18000	95000.0	48388000	▇▁▁▁▁
K10	0	1	353665.65	1589523.52	-2	2000	65000	242000.0	61852000	▇▁▁▁▁
K11	0	1	23581.48	246991.88	-2	0	0	0.0	12401000	▇▁▁▁▁
L12	0	1	80185.74	1349349.83	-2	0	0	0.0	111938000	▇▁▁▁▁
M12	0	1	11888.16	391698.39	-2	0	0	0.0	49058000	▇▁▁▁▁
MEMBERSCH	0	1	2690.61	12815.67	-9	167	641	2083.5	1014020	▇▁▁▁▁
Q11	0	1	702918.96	7421719.40	-2	0	33000	283000.0	634364000	▇▁▁▁▁
T02	0	1	2639423.57	73594947.69	-2	-2	-2	-2.0	9073697000	▇▁▁▁▁
T06	0	1	9271963.74	39717090.27	-2	-2	1187000	5982000.0	1818529000	▇▁▁▁▁
T09	0	1	203461.80	3394275.08	-2	-2	0	0.0	161332000	▇▁▁▁▁
T15	0	1	19918.86	255405.26	-2	-2	0	0.0	20748000	▇▁▁▁▁
T40	0	1	99695.20	1287236.16	-2	-2	0	0.0	110682000	▇▁▁▁▁
T99	0	1	74279.41	909702.46	-2	-2	0	0.0	58982000	▇▁▁▁▁
TCAPOUT	0	1	3279609.48	29521711.94	-2	35000	285000	1333000.0	3151607000	▇▁▁▁▁
TCURELSC	0	1	28436776.91	179053170.29	-2	2109000	7146000	22648500.0	19453219000	▇▁▁▁▁
TCURINST	0	1	17327081.34	127893156.30	-2	1171000	4198000	13455500.0	14936045000	▇▁▁▁▁
TCUROTH	0	1	1140166.50	5600778.27	-2	55500	283000	874000.0	448671000	▇▁▁▁▁
TCURSSVC	0	1	9969528.76	48510265.33	-2	776000	2588000	8035000.0	4068503000	▇▁▁▁▁
TFEDREV	0	1	4124828.42	25252763.77	-2	261000	899000	2680000.0	2047926000	▇▁▁▁▁
TLOCREV	0	1	15170253.20	97409268.54	-2	499000	3031000	10780000.0	10600597000	▇▁▁▁▁
TNONELSE	0	1	363810.50	3018302.79	-2	0	3000	109000.0	175673000	▇▁▁▁▁
TOTALEXP	0	1	34140053.39	224183538.28	-2	2408000	8445000	26676000.0	24597709000	▇▁▁▁▁
TOTALREV	0	1	33481814.83	199406458.00	-2	2434500	8493000	26767000.0	21023695000	▇▁▁▁▁
TSTREV	0	1	14186732.90	85066587.68	-2	945000	3617000	11322500.0	8375172000	▇▁▁▁▁
U11	0	1	20131.32	332556.14	-2	0	0	0.0	28720000	▇▁▁▁▁
U22	0	1	108350.53	735676.31	-2	1000	12000	56000.0	73023000	▇▁▁▁▁
U30	0	1	19000.78	222943.67	-2	0	0	0.0	19854000	▇▁▁▁▁
U50	0	1	52941.97	586124.91	-2	0	0	13000.0	48929000	▇▁▁▁▁
U97	0	1	619238.19	11118869.62	-2	6000	52000	244000.0	1421630000	▇▁▁▁▁
V10	0	1	3998687.90	38204603.80	-2	186000	870000	3139000.0	4756409000	▇▁▁▁▁
V11	0	1	1053954.52	4087112.12	-2	15000	176000	789500.0	243582000	▇▁▁▁▁
V12	0	1	336373.22	1395789.52	-2	2000	51000	247000.0	83188000	▇▁▁▁▁
V13	0	1	806004.12	4234643.26	-2	10000	118000	543000.0	321611000	▇▁▁▁▁
V14	0	1	263026.59	1390817.64	-2	2000	37000	186000.0	108630000	▇▁▁▁▁
V15	0	1	239975.18	825385.53	-2	34000	142000	269000.0	82474000	▇▁▁▁▁
V16	0	1	92164.15	323598.91	-2	8000	42000	99000.0	25761000	▇▁▁▁▁
V17	0	1	1107942.03	5242875.32	-2	63500	257000	835000.0	341063000	▇▁▁▁▁
V18	0	1	370250.30	2069478.16	-2	15000	79000	289000.0	160978000	▇▁▁▁▁
V21	0	1	978273.54	6346261.99	-2	31000	184000	681500.0	661475000	▇▁▁▁▁
V22	0	1	388822.12	3028190.56	-2	8000	68000	276000.0	327418000	▇▁▁▁▁
V23	0	1	416869.36	1827291.51	-2	0	46000	272500.0	68954000	▇▁▁▁▁
V24	0	1	167106.71	771339.05	-2	0	12000	97000.0	34626000	▇▁▁▁▁
V29	0	1	357825.95	2204270.87	-2	0	69000	259000.0	221534000	▇▁▁▁▁
V30	0	1	131409.06	743125.50	-2	0	20000	89000.0	69482000	▇▁▁▁▁
V32	0	1	4498.39	71129.59	-2	0	0	0.0	4338000	▇▁▁▁▁
V33	0	1	2696.60	12832.19	-9	165	643	2092.0	1014020	▇▁▁▁▁
V37	0	1	446569.69	2413022.59	-2	0	87000	315000.0	237834000	▇▁▁▁▁
V38	0	1	187349.27	1238275.08	-2	0	29000	122000.0	117724000	▇▁▁▁▁
V40	0	1	2712894.19	16354709.32	-2	187000	690000	2114500.0	1661029000	▇▁▁▁▁
V45	0	1	1219745.94	8586494.05	-2	35000	269000	978500.0	1024981000	▇▁▁▁▁
V60	0	1	62758.66	644426.67	-2	0	0	0.0	31334000	▇▁▁▁▁
V65	0	1	6236.98	120315.98	-2	0	0	0.0	9815000	▇▁▁▁▁
V70	0	1	201312.99	1906604.78	-2	0	0	43000.0	118338000	▇▁▁▁▁
V75	0	1	110767.97	1647700.62	-2	0	0	0.0	148516000	▇▁▁▁▁
V80	0	1	51729.23	615328.22	-2	0	0	0.0	40513000	▇▁▁▁▁
V85	0	1	248.65	33609.35	-2	0	0	0.0	4540000	▇▁▁▁▁
V90	0	1	981687.90	6017663.61	-2	43000	202000	676000.0	591836000	▇▁▁▁▁
V91	0	1	180739.64	5702851.48	-2	0	0	0.0	738402000	▇▁▁▁▁
V92	0	1	113014.75	3320447.49	-2	0	0	0.0	383908000	▇▁▁▁▁
V93	0	1	132874.73	1178546.64	-2	0	8000	70000.0	115987000	▇▁▁▁▁
W01	0	1	864911.54	8526711.15	-2	0	0	182000.0	701763000	▇▁▁▁▁
W31	0	1	2592562.11	31381207.74	-2	0	0	132000.0	3650875000	▇▁▁▁▁
W61	0	1	5791300.75	25187070.39	-2	79000	1284000	4379500.0	2178242000	▇▁▁▁▁
WEIGHT	0	1	1.00	0.00	1	1	1	1.0	1	▁▁▇▁▁
Z32	0	1	17275789.18	99544925.43	-2	1078000	4110000	13449000.0	10254583000	▇▁▁▁▁
Z33	0	1	11689210.40	76409484.15	-2	719000	2755000	9035000.0	8613741000	▇▁▁▁▁
Z34	0	1	6003717.44	46330908.53	-2	284000	1321000	4773000.0	5409968000	▇▁▁▁▁
Z35	0	1	5979280.17	39123700.39	-2	0	497000	4110500.0	4011575000	▇▁▁▁▁
Z36	0	1	1360212.70	14071446.23	-2	0	41000	776000.0	1713079000	▇▁▁▁▁
Z37	0	1	234278.74	3988673.40	-2	0	0	89000.0	524807000	▇▁▁▁▁
Z38	0	1	296060.84	1767268.94	-2	0	0	111000.0	78518000	▇▁▁▁▁
_19H	0	1	21207890.62	143384108.88	-2	0	1330000	12291500.0	11615909000	▇▁▁▁▁
_21F	0	1	2646400.13	39506333.52	-2	0	0	0.0	4342818000	▇▁▁▁▁
_31F	0	1	1937930.41	16398282.10	-2	0	138000	925000.0	1210275000	▇▁▁▁▁
_41F	0	1	21921404.30	163411365.20	-2	0	1347000	12785500.0	12642529000	▇▁▁▁▁
_61V	0	1	502922.79	6044542.90	-2	0	0	0.0	625105000	▇▁▁▁▁
_66V	0	1	437682.37	3431237.57	-2	0	0	0.0	230000000	▇▁▁▁▁
_YEAR	0	1	10.00	0.00	10	10	10	10.0	10	▁▁▇▁▁
YEAR	0	1	2010.00	0.00	2010	2010	2010	2010.0	2010	▁▁▇▁▁

Variable type: POSIXct

skim_variable	n_missing	complete_rate	min	max	median	n_unique
DL_INGESTION_DATETIME	0	1	2021-09-02 13:02:24	2021-09-02 13:02:24	2021-09-02 13:02:24	1

# Total spendings by state
total <-fiscal2010 %>% 
  group_by(STNAME) %>%
  summarise(TOTALEXP = sum(TOTALEXP))

total  %>%
  ggplot(aes(TOTALEXP, STNAME)) +
  geom_col()

#in descending order
total  %>%
  ggplot(aes(TOTALEXP, fct_reorder(STNAME, TOTALEXP))) +
  geom_col() 

#spending on instruction by state 
instruction <- fiscal2010 %>%
  group_by(STNAME) %>%
      summarise(E13 = sum(E13))

instruction  %>%
  ggplot(aes(E13, STNAME)) +
  geom_col()

instruction  %>%
  ggplot(aes(E13, fct_reorder(STNAME, E13))) +
  geom_col()

#New York spends more on instruction, while CA spends the most total

#spending on textbooks by state 
textbooks <- fiscal2010 %>%
  group_by(STNAME) %>%
      summarise(V93 = sum(V93))

textbooks  %>%
  ggplot(aes(V93, fct_reorder(STNAME, V93))) +
  geom_col()

#spending on Special Ed teachers
SpEd <- fiscal2010 %>%
  group_by(STNAME) %>%
      summarise(Z36 = sum(Z36))

SpEd  %>%
  ggplot(aes(Z36, fct_reorder(STNAME, Z36))) +
  geom_col()

#next step - figure out how to show those spendings per state as a proportion of the total spending if that state

Data Visualization 3

Amy

Research Question:

What is the relationship between the local revenue of local education agencies and students’ literacy outcomes on statewide assessments in 2010? Additional areas of exploration included (a) average total local revenue and local revenue property taxes by state, (b) the relationship between outcomes and total local revenue vs. local revenue from property taxes, (c) the relationship between both types of funding and outcomes by state, and (d) relationship between both types of revenue and outcomes of student subgroups (e.g., race/ethnicity, disability status, language proficiency, SES).

Datasets:

• EDFacts_rla_achievement_lea_2010_2019
  
• NCES_CCD_fiscal_district_2010

Variables:

EDFacts_rla_achievement_lea_2010_2019

• YEAR
• STNAM
• FIPST
• LEAID
• ALL_RLA00PCTPROF = Percentage of all students who scored at or above their state’s proficiency level on Reading/Language Arts.
• MAM_RLA03PCTPROF = Percentage of Native American students that scored at or above proficient
• MAS_RLA00PCTPROF = Percentage of Asian/Pacific Islander students that scored at or above proficient
• MBL_RLA00PCTPROF = Percentage of Black students that scored at or above proficient
• MHI_RLA00PCTPROF = Percentage of Hispanic students that scored at or above proficient
• MTR_RLA00PCTPROF = Percentage of students with Two or More Races that scored at or above proficient
• MWH_RLA03PCTPROF = Percentage of White students that scored at or above proficient
• CWD_RLA00PCTPROF = Percentage of children with disabilities that scored at or above proficient
• ECD_RLA00PCTPROF = Percentage of economically disadvantaged students that scored at or above proficient
• LEP_RLA00PCTPROF = Percentage of limited English proficient students that scored at or above proficient

NCES_CCD_fiscal_district_2010:

• NAME
• STABBR
• CENSUSID
• V33 = LEA fall membership (i.e., number of students)
• TOTALREV = LEA total revenue
• TFEDREV = LEA total federal revenue
• TSTREV = LEA total state revenue
• TLOCREV = LEA total local revenue
• T06 = LEA local revenue from property taxes

Cleaning and Wrangling

# Selected columns of interest. Filtered so that rows with suppressed values (e.g., -9, -2) for key variables of interest weren't included. 
viz3_fiscal2010 <- fiscal2010 %>% 
  select(LEAID, NAME, STABBR, CENSUSID, V33,
         TOTALREV, TFEDREV, TSTREV, TLOCREV, T06) %>% 
  filter(V33 > 0, TLOCREV > 0, T06 > 0) %>%
  rename_with(tolower) %>% 
  rename(totalstu = v33, tlocrevtaxes = t06) %>% 
  mutate(locrev_stu = tlocrev / totalstu,
         locrevtaxes_stu = tlocrevtaxes / totalstu)

# Narrowed the RLA 2010 file down to variables of interest. Selected percent proficient variables across all grades (00) for race ethnicity, disability, English Language Learner status, and economically disadvantaged subgroups. Transformed variable names to be lowercase, transformed state names to be title case, and replaced the suppressed values (e.g., PS, n/a, etc.) with NA.
viz3_rlalea00 <- rlalea_10 %>% 
  select(YEAR, 
         STNAM, 
         FIPST,
         LEAID, 
         ALL_RLA00PCTPROF, 
         MAM_RLA00PCTPROF,
         MAS_RLA00PCTPROF,
         MBL_RLA00PCTPROF,
         MHI_RLA00PCTPROF,
         MTR_RLA00PCTPROF, 
         MWH_RLA00PCTPROF,
         CWD_RLA00PCTPROF,
         ECD_RLA00PCTPROF,
         LEP_RLA00PCTPROF) %>% 
  rename_with(tolower)  %>% 
  mutate(stnam = str_to_title(stnam), 
         (across(all_rla00pctprof:lep_rla00pctprof, 
                 ~replace(., . %in% c("PS", 
                                      "n/a",    
                                      "LT50",   
                                      "LE5",    
                                      "LE20",   
                                      "LE10",   
                                      "GE99",   
                                      "GE95",   
                                      "GE90",   
                                      "GE80",   
                                      "GE50"), NA))))

# Next step was cleaning the percentage columns to change percentage ranges to average percentages. I used the method Daniel used on the course data webpage and applied it to all subgroups.

# All = across all students
viz3_rlalea00 <- viz3_rlalea00 %>% 
  tidyr::separate(all_rla00pctprof, c("all_lower", "all_upper"), sep = "-") %>% 
  mutate(
    all_upper = ifelse(is.na(all_upper), all_lower, all_upper),
    all_lower = as.numeric(all_lower),
    all_upper = as.numeric(all_upper)
    ) %>% 
  rowwise() %>% 
  mutate(meanpctprof_all = mean(c(all_lower, all_upper))) %>% 
  ungroup()

# mam = American Indian/Alaska Native
viz3_rlalea00 <- viz3_rlalea00 %>% 
  tidyr::separate(mam_rla00pctprof, c("mam_lower", "mam_upper"), sep = "-") %>% 
  mutate(
    mam_upper = ifelse(is.na(mam_upper), mam_lower, mam_upper),
    mam_lower = as.numeric(mam_lower),
    mam_upper = as.numeric(mam_upper)
    ) %>% 
  rowwise() %>% 
  mutate(meanpctprof_mam = mean(c(mam_lower, mam_upper))) %>% 
  ungroup()

# mas = Asian/Pacific Islander
viz3_rlalea00 <- viz3_rlalea00 %>% 
  tidyr::separate(mas_rla00pctprof, c("mas_lower", "mas_upper"), sep = "-") %>% 
  mutate(
    mas_upper = ifelse(is.na(mas_upper), mas_lower, mas_upper),
    mas_lower = as.numeric(mas_lower),
    mas_upper = as.numeric(mas_upper)
    ) %>% 
  rowwise() %>% 
  mutate(meanpctprof_mas = mean(c(mas_lower, mas_upper))) %>% 
  ungroup()

# mbl = Black
viz3_rlalea00 <- viz3_rlalea00 %>% 
  tidyr::separate(mbl_rla00pctprof, c("mbl_lower", "mbl_upper"), sep = "-") %>% 
  mutate(
    mbl_upper = ifelse(is.na(mbl_upper), mbl_lower, mbl_upper),
    mbl_lower = as.numeric(mbl_lower),
    mbl_upper = as.numeric(mbl_upper)
    ) %>% 
  rowwise() %>% 
  mutate(meanpctprof_mbl = mean(c(mbl_lower, mbl_upper))) %>% 
  ungroup()

# mhi = Hispanic/Latino
viz3_rlalea00 <- viz3_rlalea00 %>% 
  tidyr::separate(mhi_rla00pctprof, c("mhi_lower", "mhi_upper"), sep = "-") %>% 
  mutate(
    mhi_upper = ifelse(is.na(mhi_upper), mhi_lower, mhi_upper),
    mhi_lower = as.numeric(mhi_lower),
    mhi_upper = as.numeric(mhi_upper)
    ) %>% 
  rowwise() %>% 
  mutate(meanpctprof_mhi = mean(c(mhi_lower, mhi_upper))) %>% 
  ungroup()

# mtr = Multiracial
viz3_rlalea00 <- viz3_rlalea00 %>% 
  tidyr::separate(mtr_rla00pctprof, c("mtr_lower", "mtr_upper"), sep = "-") %>% 
  mutate(
    mtr_upper = ifelse(is.na(mtr_upper), mtr_lower, mtr_upper),
    mtr_lower = as.numeric(mtr_lower),
    mtr_upper = as.numeric(mtr_upper)
    ) %>% 
  rowwise() %>% 
  mutate(meanpctprof_mtr = mean(c(mtr_lower, mtr_upper))) %>% 
  ungroup()

# mwh = White
viz3_rlalea00 <- viz3_rlalea00 %>% 
  tidyr::separate(mwh_rla00pctprof, c("mwh_lower", "mwh_upper"), sep = "-") %>% 
  mutate(
    mwh_upper = ifelse(is.na(mwh_upper), mwh_lower, mwh_upper),
    mwh_lower = as.numeric(mwh_lower),
    mwh_upper = as.numeric(mwh_upper)
    ) %>% 
  rowwise() %>% 
  mutate(meanpctprof_mwh = mean(c(mwh_lower, mwh_upper))) %>% 
  ungroup()

# cwd = children with disabilities
viz3_rlalea00 <- viz3_rlalea00 %>% 
  tidyr::separate(cwd_rla00pctprof, c("cwd_lower", "cwd_upper"), sep = "-") %>% 
  mutate(
    cwd_upper = ifelse(is.na(cwd_upper), cwd_lower, cwd_upper),
    cwd_lower = as.numeric(cwd_lower),
    cwd_upper = as.numeric(cwd_upper)
    ) %>% 
  rowwise() %>% 
  mutate(meanpctprof_cwd = mean(c(cwd_lower, cwd_upper))) %>% 
  ungroup()

# ecd = economically disadvantaged
viz3_rlalea00 <- viz3_rlalea00 %>% 
  tidyr::separate(ecd_rla00pctprof, c("ecd_lower", "ecd_upper"), sep = "-") %>% 
  mutate(
    ecd_upper = ifelse(is.na(ecd_upper), ecd_lower, ecd_upper),
    ecd_lower = as.numeric(ecd_lower),
    ecd_upper = as.numeric(ecd_upper)
    ) %>% 
  rowwise() %>% 
  mutate(meanpctprof_ecd = mean(c(ecd_lower, ecd_upper))) %>% 
  ungroup()

# lep = limited English proficiency (English Language Learner)
viz3_rlalea00 <- viz3_rlalea00 %>% 
  tidyr::separate(lep_rla00pctprof, c("lep_lower", "lep_upper"), sep = "-") %>% 
  mutate(
    lep_upper = ifelse(is.na(lep_upper), lep_lower, lep_upper),
    lep_lower = as.numeric(lep_lower),
    lep_upper = as.numeric(lep_upper)
    ) %>% 
  rowwise() %>% 
  mutate(meanpctprof_lep = mean(c(lep_lower, lep_upper))) %>% 
  ungroup()

# Get ride of the "_lower" and "_upper" percentage columns since they won't be needed
viz3_rlalea00 <- viz3_rlalea00 %>% 
  select(year, 
         stnam, 
         fipst,
         leaid,
         contains("meanpctprof"))

# Pivoted the dataset longer to have a column for subgroup and a column for mean percentage proficient. 

viz3_rlalea00_long <- viz3_rlalea00 %>%
  pivot_longer(
        cols = contains("meanpctprof"),
        names_to = "subgroup",
        values_to = "meanpctprof",
        names_prefix = "meanpctprof_") 

# Joined the long file with the cleaned/narrowed fiscal data file. Used an inner join because I'm only interested in LEAs that have both student proficiency and fiscal data. 
viz3_rla00long_fiscal_2010 <- inner_join(viz3_rlalea00_long, viz3_fiscal2010, by = "leaid")

Potential Visualizations

These are the data visualizations I am thinking I will choose from for our final product. I’m not going to include all of these. My primary next step is to narrow them down. Please note that they are still a bit rough and need refinement (some more than others). Aside from determining which visualizations to include, I plan to finalize color, try out annotations and/or highlighting, and explore alternate options for faceting by state. If I include the plots with fitted lines, I plan to update the colors and replace the legend with annotations.

First off, bar graphs. The first two summarize LEA local revenue and LEA local revenue from property taxes averaged by state. The third graph summarizes the average percentage of students scoring at/above proficient by state.

# Bar graph: Average LEA total local revenue ($ per student) by state
viz3_rla00long_fiscal_2010 %>% 
  filter(subgroup == "all")  %>% 
  group_by(stnam) %>% 
  summarize(mean_locrev_stu = mean(locrev_stu)) %>% 
  ggplot(aes(x = mean_locrev_stu, y = fct_reorder(stnam, mean_locrev_stu))) +
  geom_col(color = "white", alpha = .6) +
  scale_x_continuous(expand = c(0, 0),
                     breaks = c(0, 2500, 5000, 7500, 10000, 12500),
                     labels = scales::dollar) +
  labs(title = "Average Local Revenue of LEAs",
       y = "State",
       x = "Dollar per student",
       caption = "Source: National Center for Education Statistics, 2010") +
  theme_minimal() +
  theme(plot.title.position = "plot",
        panel.grid.major.y = element_blank(),
        panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank())  

# Bar graph: Average LEA local revenue from property taxes ($ per student) by state
viz3_rla00long_fiscal_2010 %>% 
  filter(subgroup == "all")  %>% 
  group_by(stnam) %>% 
  summarize(mean_locrevtaxes_stu = mean(locrevtaxes_stu)) %>% 
  ggplot(aes(x = mean_locrevtaxes_stu, y = fct_reorder(stnam, mean_locrevtaxes_stu))) +
  geom_col(color = "white", alpha = .6) +
  scale_x_continuous(expand = c(0, 0),
                    breaks = c(0, 2500, 5000, 7500, 10000),
                     labels = scales::dollar) +
  labs(title = "Average Local Revenue of LEAs from Property Taxes",
       y = "State",
       x = "Dollar per student",
       caption = "Source: National Center for Education Statistics, 2010") +
  theme_minimal() +
  theme(plot.title.position = "plot",
        panel.grid.major.y = element_blank(),
        panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank())

# Bar graph of average RLA proficiency for each state
viz3_rla00long_fiscal_2010 %>% 
  filter(subgroup == "all")  %>% 
  group_by(stnam) %>% 
  summarize(mean_pctprof = mean(meanpctprof, na.rm = T)) %>% 
  ggplot(aes(x = mean_pctprof, y = fct_reorder(stnam, mean_pctprof))) +
  geom_col(color = "white", alpha = .6) +
  scale_x_continuous(expand = c(0, 0),
                    breaks = c(0, 20, 40, 60, 80),
                    labels = c("0%", "20%", "40%", "60%", "80%")) +
  labs(title = "Average Proficiency in Reading/Language Arts",
       subtitle = "Students in Grades 3 through HS",
       y = "State",
       x = "Average Percentage",
       caption = "Source: National Center for Education Statistics, 2010") +
  theme_minimal() +
  theme(plot.title.position = "plot",
        panel.grid.major.y = element_blank(),
        panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank())  

Density ridges of local revenue from property taxes for LEAs by state. One thing I plan to do, if I include this plot, that I didn’t get to is sorting the states by mean revenue.

# Density ridges of local revenue from property taxes across LEAs by state
viz3_rla00long_fiscal_2010 %>% 
  filter(subgroup == "all")  %>% 
  ggplot(aes(x = locrevtaxes_stu, y = stnam)) +
    geom_density_ridges(fill = "cornflower blue", color = "white", alpha = .8) +
    theme_minimal() +
  labs(title = "Local Revenue from Property Taxes in LEAS of Each State",
       y = "State",
       x = "Dollar per student",
       caption = "Source: National Center for Education Statistics, 2010") +
  theme_minimal() +
  theme(plot.title.position = "plot") + 
  scale_x_continuous(expand = c(0, 0)) +
  coord_cartesian(xlim = c(0, 25000))

Scatterplots showing the relationship between types of revenue and average percentage of students scoring at/above proficient on statewide assessments of reading/language arts. Note that I used a log transformation of the x-axis to spread the points out. Without the transformation, the bulk of the points were clustered near the bottom of the range. I think that I could use highlighting and annotations for the lowest and highest points.

# Scatterplots: All students, LEA total local revenue ($ per stu) and LEA local revenue from property taxes by approx. pct proficient
viz3_rla00long_fiscal_2010 %>% 
  filter(subgroup == "all") %>% 
  ggplot(aes(x = locrev_stu, y = meanpctprof)) +
  geom_point(color = "gray30", fill = "gray30", alpha = .4) + 
  scale_x_log10(labels = scales::dollar) +
  labs(title = "Relationship between Local Revenue and RLA Proficiency",
       y = "Approximate Average Percent Proficient",
       x = "Dollar per Student",
       caption = "Source: National Center for Education Statistics, 2010") +
  theme_minimal() +
  theme(plot.title.position = "plot",
        panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank()) 

viz3_rla00long_fiscal_2010 %>% 
  filter(subgroup == "all") %>% 
  ggplot(aes(x = locrevtaxes_stu, y = meanpctprof)) +
  geom_point(color = "gray30", fill = "gray30", alpha = .4) + 
  scale_x_log10(labels = scales::dollar) +
  labs(title = "Relationship between Local Revenue from Property Taxes and RLA Proficiency",
       y = "Approximate Average Percent Proficient",
       x = "Dollar per Student",
       caption = "Source: National Center for Education Statistics, 2010") +
  theme_minimal() +
  theme(plot.title.position = "plot",
        panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank())

Scatterplots faceted by state.

# Scatterplot: All students - relationship between local revenue ($ per student) and mean % proficient, faceted by state
viz3_rla00long_fiscal_2010 %>% 
  filter(subgroup == "all") %>% 
  ggplot(aes(x = locrev_stu, y = meanpctprof)) +
  facet_wrap(~stnam) +
  geom_point(color = "gray30", fill = "gray30", alpha = .4) + 
  scale_x_log10(labels = scales::dollar) +
  labs(title = "Relationship between Total Local Revenue and RLA Proficiency",
       y = "Approximate Average Percent Proficient",
       x = "Dollar per Student",
       caption = "Source: National Center for Education Statistics, 2010") +
  theme_minimal() +
  theme(plot.title.position = "plot",
        panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank()) 

# Scatterplot: All students - relationship between local revenue from property taxes ($ per student) and mean % proficient, faceted by state
viz3_rla00long_fiscal_2010 %>% 
  filter(subgroup == "all") %>% 
  ggplot(aes(x = locrevtaxes_stu, y = meanpctprof)) +
  facet_wrap(~stnam) +
  geom_point(color = "gray30", fill = "gray30", alpha = .4) + 
  scale_x_log10(labels = scales::dollar) +
  labs(title = "Relationship between Local Revenue from Property Taxes and RLA Proficiency",
       y = "Approximate Average Percent Proficient",
       x = "Dollar per Student",
       caption = "Source: National Center for Education Statistics, 2010") +
  theme_minimal() +
  theme(plot.title.position = "plot",
        panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank())

Scatterplots showing the relationship between revenue and outcomes, faceted by subgroup. These are still pretty rough (e.g., labels need work).

# Scatterplot: Relationship between local revenue ($ per student) and mean % proficient, faceted by subgroup
viz3_rla00long_fiscal_2010 %>% 
  ggplot(aes(x = locrev_stu, y = meanpctprof)) +
  facet_wrap(~subgroup) +
  geom_point(color = "gray30", fill = "gray30", alpha = .4) + 
  scale_x_log10(labels = scales::dollar) +
  labs(title = "Relationship between Total Local Revenue and RLA Proficiency",
       y = "Approximate Average Percent Proficient",
       x = "Dollar per Student",
       caption = "Source: National Center for Education Statistics, 2010") +
  theme_minimal() +
  theme(plot.title.position = "plot",
        panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank()) 

# Scatterplot: Relationship between local revenue from property taxes ($ per student) and mean % proficient, faceted by subgroup
viz3_rla00long_fiscal_2010 %>% 
  ggplot(aes(x = locrevtaxes_stu, y = meanpctprof)) +
  facet_wrap(~subgroup) +
  geom_point(color = "gray30", fill = "gray30", alpha = .4) +
  scale_x_log10(labels = scales::dollar) +
  labs(title = "Relationship between Local Revenue from Property Taxes and RLA Proficiency",
       y = "Approximate Average Percent Proficient",
       x = "Dollar per Student",
       caption = "Source: National Center for Education Statistics, 2010") +
  theme_minimal() +
  theme(plot.title.position = "plot",
        panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank())

Playing around with fitting lines. These are still rough. I need to work on the legend and want to try replacing it with annotations. Also could consider changing the color to highlight a specific group or pick a different color palette.

viz3_rla00long_fiscal_2010 %>% 
  filter(subgroup != "all") %>% 
  ggplot() +
  geom_smooth(method = lm, se = F, aes(x = locrevtaxes_stu, y = meanpctprof, color = subgroup)) +
  scale_x_log10(labels = scales::dollar) +
  labs(title = "Relationship between Local Revenue from Property Taxes and RLA Proficiency",
       y = "Approximate Average Percent Proficient",
       x = "Dollar per Student",
       caption = "Source: National Center for Education Statistics, 2010") +
  theme_minimal() +
  theme(plot.title.position = "plot",
        panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank())

Fitted lines faceted by state (also very rough).

viz3_rla00long_fiscal_2010 %>% 
  filter(subgroup != "all") %>% 
  ggplot() +
  geom_smooth(method = lm, se = F, aes(x = locrevtaxes_stu, y = meanpctprof, color = subgroup)) +
  facet_wrap(~stnam) +
  scale_x_log10(labels = scales::dollar) +
  labs(title = "Relationship between Local Revenue from Property Taxes and RLA Proficiency",
       y = "Approximate Average Percent Proficient",
       x = "Dollar per Student",
       caption = "Source: National Center for Education Statistics, 2010") +
  theme_minimal() +
  theme(plot.title.position = "plot",
        panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank())

Created maps displaying (a) average LEA local revenue in each state and (b) average LEA local revenue from property taxes in each state. I still need to fill in the missing states and want to try out different color palettes

viz3_us <- usa_sf() 

viz3_map <- left_join(viz3_rla00long_fiscal_2010, 
                      viz3_us, 
                      by = c("stnam" = "name"))

viz3_map %>% 
  filter(subgroup == "all") %>% 
  group_by(stnam) %>% 
  mutate(mean_locrev_stu = mean(locrev_stu)) %>% 
  ggplot(aes(geometry = geometry, fill = mean_locrev_stu)) +
  geom_sf(color = "white", size = 0) +
    scale_fill_viridis(option = "magma", 
                       name = "Dollar per student",
                                         breaks = c(0, 2500, 5000, 7500, 10000, 12500),
                                         labels = c("$0", 
                                                    "$2,500", 
                                                    "$5,000", 
                                                    "$7,500", 
                                                    "$10,000",
                                                    "$12,5000")) +
  theme_void() +
  labs(title = "Average LEA Total Local Revenue",
       caption = "Source: National Center for Education Statistics, 2010") +
  theme(plot.title.position = "plot") 

viz3_map %>% 
  filter(subgroup == "all") %>% 
  group_by(stnam) %>% 
  mutate(mean_locrevtaxes_stu = mean(locrevtaxes_stu)) %>% 
  ggplot(aes(geometry = geometry, fill = mean_locrevtaxes_stu)) +
  geom_sf(color = "white", size = 0) +
    scale_fill_viridis(option = "magma", 
                       name = "Dollar per student",
                                         breaks = c(0, 2500, 5000, 7500, 10000),
                                         labels = c("$0", 
                                                    "$2,500", 
                                                    "$5,000", 
                                                    "$7,500", 
                                                    "$10,000")) +
  theme_void() +
  labs(title = "Average LEA Local Revenue from Property Taxes",
       caption = "Source: National Center for Education Statistics, 2010") +
  theme(plot.title.position = "plot") 

Other Data Visualizations

These are some of the preliminary visualizations I did that I don’t think I’m moving forward with.

# Scatterplot: Total local LEA revenue from property taxes ($ per stu) x approx. pct proficient, color = subgroup
viz3_rla00long_fiscal_2010 %>% 
  filter(subgroup != "all") %>% 
  ggplot(aes(x = locrevtaxes_stu, y = meanpctprof, color = subgroup)) +
  geom_point(alpha = .4) + 
  scale_x_log10(labels = scales::dollar) +
  theme_minimal() +
  labs(title = "Revenue from property tax x meanpctprof, color by subgroup") +
  theme(plot.title.position = "plot",
        panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank())

Fitted lines for specific states:

# fitted lines in a few specific states
viz3_rla00long_fiscal_2010 %>% 
  filter(stnam == "Montana") %>% 
  ggplot() +
  geom_smooth(method = lm, se = F, aes(x = locrevtaxes_stu, y = meanpctprof, color = subgroup)) +
  scale_x_log10(labels = scales::dollar) +
  theme_minimal() +
  theme(plot.title.position = "plot",
        panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank())  +
  labs(title = "Montana")

viz3_rla00long_fiscal_2010 %>% 
  filter(stnam == "South Dakota") %>% 
  ggplot() +
  geom_smooth(method = lm, se = F, aes(x = locrevtaxes_stu, y = meanpctprof, color = subgroup)) +
  scale_x_log10(labels = scales::dollar) +
  theme_minimal() +
  theme(plot.title.position = "plot",
        panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank()) +
  labs(title = "South Dakota")

viz3_rla00long_fiscal_2010 %>% 
  filter(stnam == "New Jersey") %>% 
  ggplot() +
  geom_smooth(method = lm, se = F, aes(x = locrevtaxes_stu, y = meanpctprof, color = subgroup)) +
  scale_x_log10(labels = scales::dollar) +
  theme_minimal() +
  theme(plot.title.position = "plot",
        panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank()) +
  labs(title = "New Jersey")

# Fitting a line over data points for local revenue x meanpctprof by state
viz3_rla00long_fiscal_2010 %>% 
  filter(subgroup == "all") %>% 
  ggplot(aes(x = locrev_stu, y = meanpctprof)) +
  geom_smooth(method = "lm") +
  facet_wrap(~stnam) +
  geom_point(alpha = .1) + 
  scale_x_log10(labels = scales::dollar) 

# Scatterplot: Relationship between local revenue from property taxes ($ per student) and mean % proficient, faceted by state, color by subgroup 
viz3_rla00long_fiscal_2010 %>% 
  filter(subgroup != "all") %>% 
  ggplot(aes(x = locrev_stu, y = meanpctprof, color = subgroup)) +
  facet_wrap(~stnam) +
  geom_point(alpha = .4) + 
  scale_x_log10(labels = scales::dollar)

Scatterplots for each student subgroup between local revenue from property tax and % proficient, faceted by state. The subgroup is indicated in the title.

# Scatterplots: Relationship between local revenue from property taxes ($ per student) and mean % proficient, faceted by state for each subgroup 

viz3_rla00long_fiscal_2010 %>% 
  filter(subgroup == "mam") %>% 
  ggplot(aes(x = locrev_stu, y = meanpctprof)) +
  facet_wrap(~stnam) +
  geom_point(alpha = .4) + 
  scale_x_log10(labels = scales::dollar) +
  labs(title = "American Indian/Alaska Native")

viz3_rla00long_fiscal_2010 %>% 
  filter(subgroup == "mas") %>% 
  ggplot(aes(x = locrev_stu, y = meanpctprof)) +
  facet_wrap(~stnam) +
  geom_point(alpha = .4) + 
  scale_x_log10(labels = scales::dollar) +
  labs(title = "Asian/Pacific Islander")

viz3_rla00long_fiscal_2010 %>% 
  filter(subgroup == "mhi") %>% 
  ggplot(aes(x = locrev_stu, y = meanpctprof)) +
  facet_wrap(~stnam) +
  geom_point(alpha = .4) + 
  scale_x_log10(labels = scales::dollar) +
  labs(title = "Hispanic/Latino")

viz3_rla00long_fiscal_2010 %>% 
  filter(subgroup == "mbl") %>% 
  ggplot(aes(x = locrev_stu, y = meanpctprof)) +
  facet_wrap(~stnam) +
  geom_point(alpha = .4) + 
  scale_x_log10(labels = scales::dollar) +
  labs(title = "Black")

viz3_rla00long_fiscal_2010 %>% 
  filter(subgroup == "mwh") %>% 
  ggplot(aes(x = locrev_stu, y = meanpctprof)) +
  facet_wrap(~stnam) +
  geom_point(alpha = .4) + 
  scale_x_log10(labels = scales::dollar) +
  labs(title = "White")

viz3_rla00long_fiscal_2010 %>% 
  filter(subgroup == "mtr") %>% 
  ggplot(aes(x = locrev_stu, y = meanpctprof)) +
  facet_wrap(~stnam) +
  geom_point(alpha = .4) + 
  scale_x_log10(labels = scales::dollar) +
  labs(title = "Multiracial")

viz3_rla00long_fiscal_2010 %>% 
  filter(subgroup == "cwd") %>% 
  ggplot(aes(x = locrev_stu, y = meanpctprof)) +
  facet_wrap(~stnam) +
  geom_point(alpha = .4) + 
  scale_x_log10(labels = scales::dollar) +
  labs(title = "Students with Disabilities")

viz3_rla00long_fiscal_2010 %>% 
  filter(subgroup == "ecd") %>% 
  ggplot(aes(x = locrev_stu, y = meanpctprof)) +
  facet_wrap(~stnam) +
  geom_point(alpha = .4) + 
  scale_x_log10(labels = scales::dollar) +
  labs(title = "Economically Disadvantaged")

viz3_rla00long_fiscal_2010 %>% 
  filter(subgroup == "lep") %>% 
  ggplot(aes(x = locrev_stu, y = meanpctprof)) +
  facet_wrap(~stnam) +
  geom_point(alpha = .4) + 
  scale_x_log10(labels = scales::dollar) +
  labs(title = "Limited English Proficiency")